**************************************************************
*HURRICANES AND GAS GOUGING - APPENDIX PRICE AND MARGIN REGRESSIONS
**************************************************************


********************************************************************************
*FIGURE B.5: TREATMENT EFFECT HETEROGENEITY (DIFFERENCE-IN-DIFFERENCES)  
********************************************************************************

*****************
*PANEL (A): BRANDED/RETAILER/UNBRANDED
reghdfe retail pre_hur_brand hur_brand post_hur_brand ///
               pre_hur_ret hur_ret post_hur_ret ///
			   pre_hur_unbrand hur_unbrand post_hur_unbrand ///
			   wholesale CTST CHRT CTSN CHRN temp temp2, ///
			   absorb(station_id year month dow) cluster(county_FIPS)	
estimates store brand

coefplot(brand), ///
	drop(_cons wholesale L* D* temp temp2 CTST CHRT CTSN CHRN) ///
	xline(0, lc(cranberry)) graphregion(fcolor(white)) ///
	order(pre_hur_brand pre_hur_ret pre_hur_unbrand ///
	      hur_brand hur_ret hur_unbrand ///
	      post_hur_brand post_hur_ret post_hur_unbrand) ///
	m(D) mfcolor(white) mlcolor(edkblue) msize(medlarge) lcolor(edkblue)   ///
	headings(pre_hur_brand ="{bf:Pre-Hurricane}" ///
		   hur_brand="{bf: Hurricane}" ///
		   post_hur_brand="{bf: Post-Hurricane}", labcolor(edkblue) labsize(medlarge)) ///
	coeflabels(pre_hur_brand="Branded" hur_brand="Branded" post_hur_brand="Branded" ///
			   pre_hur_ret="Retailer" hur_ret="Retailer" post_hur_ret="Retailer" ///
			   pre_hur_unbrand="Independent" hur_unbrand="Independent" post_hur_unbrand="Independent", ///
				notick labsize(small))	   
graph export $figs/coef_brand_margin_$outputdate.png, replace width(4000)

*****************
*PANEL (B): LOCAL COMPETITION
reghdfe retail pre_hur_comp_hi pre_hur_comp_mid pre_hur_comp_lo ///
			   hur_comp_hi hur_comp_mid hur_comp_lo ///
			   post_hur_comp_hi post_hur_comp_mid post_hur_comp_lo ///
			   wholesale CTST CHRT CTSN CHRN temp temp2, ///
			   absorb(station_id year month dow) cluster(county_FIPS)
estimates store comp

coefplot(comp), ///
	drop(_cons wholesale L* D* temp temp2 CTST CHRT CTSN CHRN) ///
	xline(0, lc(cranberry)) graphregion(fcolor(white)) ///
	order(pre_hur_comp_lo pre_hur_comp_mid pre_hur_comp_hi ///
	      hur_comp_lo hur_comp_mid hur_comp_hi ///
	      post_hur_comp_lo post_hur_comp_mid post_hur_comp_hi) ///
	m(D) mfcolor(white) mlcolor(edkblue) msize(medlarge) lcolor(edkblue)   ///
	headings(pre_hur_comp_lo ="{bf:Pre-Hurricane}" ///
		   hur_comp_lo="{bf: Hurricane}" ///
		   post_hur_comp_lo="{bf: Post-Hurricane}", labcolor(edkblue) labsize(medlarge)) ///
	coeflabels(pre_hur_comp_lo="0 Firms w/in 5 km" hur_comp_lo="0 Firms w/in 5 km" post_hur_comp_lo="0 Firms w/in 5 km" ///
			   pre_hur_comp_mid="1-2 Firms w/in 5 km" hur_comp_mid="1-2 Firms w/in 5 km" post_hur_comp_mid="1-2 Firms w/in 5 km" ///
			   pre_hur_comp_hi=">2 Firms w/in 5 km" hur_comp_hi=">2 Firms w/in 5 km" post_hur_comp_hi=">2 Firms w/in 5 km", ///
			   notick labsize(small))		   
graph export $figs/coef_competition_margin_$outputdate.png, replace width(4000)

*****************
*PANEL (C): DISTANCE TO HIGHWAY
reghdfe retail pre_hur_hw_close pre_hur_hw_mid pre_hur_hw_far   ///
			   hur_hw_close hur_hw_mid hur_hw_far  ///
               post_hur_hw_close post_hur_hw_mid post_hur_hw_far ///
			   wholesale CTST CHRT CTSN CHRN temp temp2, ///
			   absorb(station_id year month dow) cluster(county_FIPS)	
estimates store highway

coefplot(highway), ///
	drop(_cons wholesale L* D* temp temp2 CTST CHRT CTSN CHRN) ///
	xline(0, lc(cranberry)) graphregion(fcolor(white)) ///
	order(pre_hur_hw_close pre_hur_hw_mid pre_hur_hw_far ///
	      hur_hw_close hur_hw_mid hur_hw_far ///
	      post_hur_hw_close post_hur_hw_mid post_hur_hw_far) ///
	m(D) mfcolor(white) mlcolor(edkblue) msize(medlarge) lcolor(edkblue)   ///
	headings(pre_hur_hw_close ="{bf:Pre-Hurricane}" ///
		   hur_hw_close="{bf: Hurricane}" ///
		   post_hur_hw_close="{bf: Post-Hurricane}", labcolor(edkblue) labsize(medlarge)) ///
	coeflabels(pre_hur_hw_close="Highway <0.01 km" hur_hw_close="Highway <0.01 km" post_hur_hw_close="Highway <0.01 km" ///
			   pre_hur_hw_mid="Highway 0.01 to 2.5 km" hur_hw_mid="Highway 0.01 to 2.5 km" post_hur_hw_mid="Highway 0.01 to 2.5 km" ///
			   pre_hur_hw_far="Highway > 2.5 km" hur_hw_far="Highway > 2.5 km" post_hur_hw_far="Highway > 2.5 km", ///
				notick labsize(small))		   
graph export $figs/coef_highway_margin_$outputdate.png, replace width(4000)
		 

********************************************************************************
*FIGURE B.6: WHOLESALE RACK MARGINS
********************************************************************************

*****************
*PANEL (A): ALL HURRICANES
frame copy default rack_regs, replace
frame change rack_regs
keep if sample_main==1
gsort station_id date

*Racks selling to landfall stations (Full Sample): 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 15, 16, 18 
gcollapse (mean) wholesale bulk temp temp2 (first) year month dow county_FIPS, by(nearRack1 date) 
keep if nearRack1==3|nearRack1==4|nearRack1==5|nearRack1==6|nearRack1==7|nearRack1==8|nearRack1==9|nearRack1==11|nearRack1==12|nearRack1==13|nearRack1==14|nearRack1==15|nearRack1==16|nearRack1==18 	
		
sort nearRack1 date
gen hur_landfall_d0=0 
	replace hur_landfall_d0=1 if date==`=td(12aug2004)'
	replace hur_landfall_d0=1 if date==`=td(06sep2004)'
	replace hur_landfall_d0=1 if date==`=td(16sep2004)'
	replace hur_landfall_d0=1 if date==`=td(26sep2004)'
	replace hur_landfall_d0=1 if date==`=td(11jun2005)'
	replace hur_landfall_d0=1 if date==`=td(10jul2005)'
	replace hur_landfall_d0=1 if date==`=td(25aug2005)'
	replace hur_landfall_d0=1 if date==`=td(29aug2005)'
	replace hur_landfall_d0=1 if date==`=td(24sep2005)'
	replace hur_landfall_d0=1 if date==`=td(24oct2005)'
	replace hur_landfall_d0=1 if date==`=td(13jun2006)'
	replace hur_landfall_d0=1 if date==`=td(13sep2007)'
	replace hur_landfall_d0=1 if date==`=td(01sep2008)'
	replace hur_landfall_d0=1 if date==`=td(13sep2008)'
	
xtset nearRack1 date

****
*Creating 14-day event windows   
gen event_day=. //Event day variable
	replace event_day=0 if hur_landfall_d0==1
	
*Indicators: 14 Days Prior to Hurricane for Different Samples
forvalues t = 1/14 {
	local n=-1*(`t')
	gen y=f`t'.year
	gen hur_landfall_dn`t' = 0
		replace hur_landfall_dn`t' = 1 if f`t'.hur_landfall_d0==1 & y==year
	replace event_day=`n' if hur_landfall_dn`t'==1
	drop y
}
*
*Indicators: 14 Days After Hurricane
forvalues t = 1/14 {
	gen y=l`t'.year
	gen hur_landfall_d`t' = 0		
		replace hur_landfall_d`t' = 1 if l`t'.hur_landfall_d0==1 & y==year
	replace event_day=`t' if hur_landfall_d`t'==1
	drop y		
}
*
order hur_landfall_dn14 hur_landfall_dn13 hur_landfall_dn12 hur_landfall_dn11 ///
      hur_landfall_dn10 hur_landfall_dn9 hur_landfall_dn8 hur_landfall_dn7 ///
	  hur_landfall_dn6 hur_landfall_dn5 hur_landfall_dn4 hur_landfall_dn3 ///
	  hur_landfall_dn2 hur_landfall_dn1 hur_landfall_d0 hur_landfall_d1 ///
	  hur_landfall_d2 hur_landfall_d3 hur_landfall_d4 hur_landfall_d5 ///
	  hur_landfall_d6 hur_landfall_d7 hur_landfall_d8 hur_landfall_d9 ///
	  hur_landfall_d10 hur_landfall_d11 hur_landfall_d12 hur_landfall_d13 ///
	  hur_landfall_d14, last		
egen window_landfall=rowtotal(hur_landfall_dn14-hur_landfall_d14)  //Event study - Landfall
* 
gen d=. in 1
gen xb_hur1 = . in 1
	gen hi_hur1 = . in 1
	gen lo_hur1 = . in 1	
*
*Hurricane Landfall Areas  
reghdfe wholesale hur_landfall_dn14-hur_landfall_d14 ///
		 temp temp2 bulk if window_landfall>=1, ///
	    absorb(nearRack1 year month dow) cluster(nearRack1)
*
replace d=-14 in 1
replace xb_hur1=0 in 1
replace hi_hur1=0 in 1
replace lo_hur1=0 in 1
*
forvalues i = 2/14 {
	local j=-1*(`i'-15)
	replace d=-1*`j' in `i'	
	lincom hur_landfall_dn`j'-hur_landfall_dn14
	replace xb_hur1    = r(estimate) in `i'
	replace hi_hur1 = r(estimate) + 1.96*r(se)  in `i'
	replace lo_hur1 = r(estimate) - 1.96*r(se) in `i'

}
*
forvalues i = 0/14 {
	local j=`i'+15
	replace d=`i' in `j'
	lincom hur_landfall_d`i'-hur_landfall_dn14
	replace xb_hur1    = r(estimate) in `j'
	replace hi_hur1 = r(estimate) + 1.96*r(se)  in `j'
	replace lo_hur1 = r(estimate) - 1.96*r(se) in `j'
}
*
*Graph
tw(connected xb_hur1 d, m(T) mfcolor(white) mlcolor(black) msize(medium) lcolor(edkblue) lwidth(medthick) mlwidth(medium)) ///
	(line hi_hur1 d, lpattern(dash) lcolor(erose)) ///
	(line lo_hur1 d, lpattern(dash) lcolor(erose)), ///
	graphr(color(white)) ///
	yscale(noline) ///
	xtit("Days Before/After Hurricane Landfall") ///
	ytit("Margin ($/gal)") ///
	ylabel(-0.1(0.05)0.1 ,nogrid angle(0)) ///
	xlabel(-14(2)14) ///
	yline(0, lcolor(black))   ///
	xline(0,lcolor(black) lp(dash)) ///
	legend(off)  
graph export $figs/event_rack1_$outputdate.png, replace width(4000)
*
*****************
*PANEL (B): ALL HURRICANES EXCEPT FOR KATRINA/RITA/IKE
frame change default
frame copy default rack_regs, replace
frame change rack_regs
keep if sample_main==1
gsort station_id date

*
*Racks selling to landfall stations: 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 15, 16, 18 
gcollapse (mean) wholesale bulk temp temp2 (first) year month dow county_FIPS, by(nearRack1 date) 
keep if nearRack1==3|nearRack1==4|nearRack1==5|nearRack1==6|nearRack1==7|nearRack1==8|nearRack1==9|nearRack1==11|nearRack1==12|nearRack1==13|nearRack1==14|nearRack1==15|nearRack1==16|nearRack1==18 	
*	
*Creating day 0 event-study indicators
sort nearRack1 date
gen hur_landfall_d0=0 
	replace hur_landfall_d0=1 if date==`=td(12aug2004)'
	replace hur_landfall_d0=1 if date==`=td(06sep2004)'
	replace hur_landfall_d0=1 if date==`=td(16sep2004)'
	replace hur_landfall_d0=1 if date==`=td(26sep2004)'
	replace hur_landfall_d0=1 if date==`=td(11jun2005)'
	replace hur_landfall_d0=1 if date==`=td(10jul2005)'
	replace hur_landfall_d0=1 if date==`=td(24oct2005)'
	replace hur_landfall_d0=1 if date==`=td(13jun2006)'
	replace hur_landfall_d0=1 if date==`=td(13sep2007)'
	replace hur_landfall_d0=1 if date==`=td(01sep2008)'
xtset nearRack1 date
*
*Creating 14-day event windows   
gen event_day=. //Event day variable
	replace event_day=0 if hur_landfall_d0==1
*	
*Indicators: 14 Days Prior to Hurricane for Different Samples
forvalues t = 1/14 {
	local n=-1*(`t')
	*Year check
	gen y=f`t'.year
	*Landfall 
	gen hur_landfall_dn`t' = 0
		replace hur_landfall_dn`t' = 1 if f`t'.hur_landfall_d0==1 & y==year
	*Event day  
	replace event_day=`n' if hur_landfall_dn`t'==1
	drop y
}
*
*Indicators: 14 Days After Hurricane
forvalues t = 1/14 {
	*Year check
	gen y=l`t'.year
	*Landfall indicator
	gen hur_landfall_d`t' = 0		
		replace hur_landfall_d`t' = 1 if l`t'.hur_landfall_d0==1 & y==year
	*Event day  
	replace event_day=`t' if hur_landfall_d`t'==1
	drop y		
}
*
order hur_landfall_dn14 hur_landfall_dn13 hur_landfall_dn12 hur_landfall_dn11 ///
      hur_landfall_dn10 hur_landfall_dn9 hur_landfall_dn8 hur_landfall_dn7 ///
	  hur_landfall_dn6 hur_landfall_dn5 hur_landfall_dn4 hur_landfall_dn3 ///
	  hur_landfall_dn2 hur_landfall_dn1 hur_landfall_d0 hur_landfall_d1 ///
	  hur_landfall_d2 hur_landfall_d3 hur_landfall_d4 hur_landfall_d5 ///
	  hur_landfall_d6 hur_landfall_d7 hur_landfall_d8 hur_landfall_d9 ///
	  hur_landfall_d10 hur_landfall_d11 hur_landfall_d12 hur_landfall_d13 ///
	  hur_landfall_d14, last		
egen window_landfall=rowtotal(hur_landfall_dn14-hur_landfall_d14)  //Event study - Landfall

*****
*EVENT STUDY - RACK MARGINS  
gen d=. in 1
gen xb_hur1 = . in 1
	gen hi_hur1 = . in 1
	gen lo_hur1 = . in 1	

*****
*Hurricane Landfall Areas  
reghdfe wholesale hur_landfall_dn14-hur_landfall_d14 ///
		 temp temp2 bulk if window_landfall>=1, ///
	    absorb(nearRack1 year month dow) cluster(nearRack1)
*
replace d=-14 in 1
replace xb_hur1=0 in 1
replace hi_hur1=0 in 1
replace lo_hur1=0 in 1
*
forvalues i = 2/14 {
	local j=-1*(`i'-15)
	replace d=-1*`j' in `i'	
	lincom hur_landfall_dn`j'-hur_landfall_dn14
	replace xb_hur1    = r(estimate) in `i'
	replace hi_hur1 = r(estimate) + 1.96*r(se)  in `i'
	replace lo_hur1 = r(estimate) - 1.96*r(se) in `i'

}
*
forvalues i = 0/14 {
	local j=`i'+15
	replace d=`i' in `j'
	lincom hur_landfall_d`i'-hur_landfall_dn14
	replace xb_hur1    = r(estimate) in `j'
	replace hi_hur1 = r(estimate) + 1.96*r(se)  in `j'
	replace lo_hur1 = r(estimate) - 1.96*r(se) in `j'
}
*
*Graph
tw(connected xb_hur1 d, m(D) mfcolor(white) mlcolor(black) msize(medium) lcolor(dkorange) lwidth(medthick) mlwidth(medium)) ///
	(line hi_hur1 d, lpattern(dash) lcolor(erose)) ///
	(line lo_hur1 d, lpattern(dash) lcolor(erose)), ///
	graphr(color(white)) ///
	yscale(noline) ///
	xtit("Days Before/After Hurricane Landfall") ///
	ytit("Margin ($/gal)") ///
	ylabel(-0.1(0.05)0.1 ,nogrid angle(0)) ///
	xlabel(-14(2)14) ///
	yline(0, lcolor(black))   ///
	xline(0,lcolor(black) lp(dash)) ///
	legend(off)  
graph export $figs/event_rack3_$outputdate.png, replace width(4000)
*
frame change default


********************************************************************************
*FIGURE B.7: ASYMMETRIC COST PASS-THROUGH
********************************************************************************
frame copy default asym_regs, replace
frame change asym_regs
keep if sample_main==1
gsort station_id date
*
gen dwholesale_p=max(d.wholesale,0)
gen dwholesale_n=min(d.wholesale,0)
*
*Landfall - 30 Day Window Sample
gen landfall_hur_sample=0 
	replace landfall_hur_sample=1 if BONCHAR_landfall==1 & date==`=td(12aug2004)'
	replace landfall_hur_sample=1 if FRANCES_landfall==1 & date==`=td(06sep2004)'
	replace landfall_hur_sample=1 if IVAN_landfall==1 & date==`=td(16sep2004)'
	replace landfall_hur_sample=1 if JEANNE_landfall==1 & date==`=td(26sep2004)'
	replace landfall_hur_sample=1 if ARLENE_landfall==1 & date==`=td(11jun2005)'
	replace landfall_hur_sample=1 if DENNIS_landfall==1 & date==`=td(10jul2005)'
	replace landfall_hur_sample=1 if KATRINA_FL_landfall==1 & date==`=td(25aug2005)'
	replace landfall_hur_sample=1 if KATRINA_LA_landfall==1 & date==`=td(29aug2005)'
	replace landfall_hur_sample=1 if RITA_landfall==1 & date==`=td(24sep2005)'
	replace landfall_hur_sample=1 if WILMA_landfall==1 & date==`=td(24oct2005)'
	replace landfall_hur_sample=1 if ALBERTO_landfall==1 & date==`=td(13jun2006)'
	replace landfall_hur_sample=1 if HUMBERTO_landfall==1 & date==`=td(13sep2007)'
	replace landfall_hur_sample=1 if GUSTAV_landfall==1 & date==`=td(01sep2008)'
	replace landfall_hur_sample=1 if IKE_landfall==1 & date==`=td(13sep2008)'
*
*Pre-hurricane indicators
gen hur_temp=0 //First day of hurricane
	replace hur_temp=1 if landfall_hur_sample==1 & l.landfall_hur_sample==0
forvalues t = 1/30 {
	gen y=f`t'.year
	replace landfall_hur_sample=1 if f`t'.hur_temp==1 & y==year
	drop y
}
*
drop hur_temp

*Post-hurricane indicators
gen hur_temp=0 //Last day of hurricane
	replace hur_temp=1 if landfall_hur_sample==1 & f.landfall_hur_sample==0	
forvalues t = 1/30 {
	gen y=l`t'.year
	replace landfall_hur_sample=1 if l`t'.hur_temp==1 & y==year
	drop y
}
*
drop hur_temp
*Landfall Sample
gen landfall_sample=0 
	replace landfall_sample=1 if BONCHAR_landfall==1 
	replace landfall_sample=1 if FRANCES_landfall==1  
	replace landfall_sample=1 if IVAN_landfall==1 
	replace landfall_sample=1 if JEANNE_landfall==1  
	replace landfall_sample=1 if ARLENE_landfall==1 
	replace landfall_sample=1 if DENNIS_landfall==1  
	replace landfall_sample=1 if KATRINA_FL_landfall==1 
	replace landfall_sample=1 if KATRINA_LA_landfall==1  
	replace landfall_sample=1 if RITA_landfall==1  
	replace landfall_sample=1 if WILMA_landfall==1 
	replace landfall_sample=1 if ALBERTO_landfall==1  
	replace landfall_sample=1 if HUMBERTO_landfall==1  
	replace landfall_sample=1 if GUSTAV_landfall==1  
	replace landfall_sample=1 if IKE_landfall==1  
gen cons=1
gen event_d=.
	label var event_d "Event Day"
gen pt1=.
	gen pt1_95l=.
	gen pt1_95u=.
	label var pt1 "Landfall"
gen pt2=.
	gen pt2_95l=.
	gen pt2_95u=.
	label var pt2 "Landfall, Hurricane"
gen pt1p=.
	gen pt1p_95l=.
	gen pt1p_95u=.
	label var pt1p "Landfall (Postive)"	
gen pt1n=.
	gen pt1n_95l=.
	gen pt1n_95u=.
	label var pt1n "Landfall (Negative)"
gen pt2p=.
	gen pt2p_95l=.
	gen pt2p_95u=.
	label var pt2p "Landfall, Hurricane (Positive)"
gen pt2n=.
	gen pt2n_95l=.
	gen pt2n_95u=.
	label var pt2n "Landfall, Hurricane (Negative)"	
*
forvalues i = 0/30 {
	local j=`i'+1
	replace event_d=`i' in `j'
}
*	
*Staggering
gen event_d1=event_d+0.1
gen event_d2=event_d-0.1
*
*
*****************
*PANEL (A): ALL STATIONS, ALL PERIODS
reghdfe d.retail l(0/30).dwholesale_p l(0/30).dwholesale_n temp temp2, ///
		absorb(cons) cluster(county_FIPS)
*
*Positive Shocks
	replace pt1p=_b[dwholesale_p] in 1
		replace pt1p_95l=_b[dwholesale_p]- 1.96*_se[dwholesale_p] in 1
		replace pt1p_95u=_b[dwholesale_p]+ 1.96*_se[dwholesale_p] in 1
	lincom dwholesale_p+L1.dwholesale_p
		replace pt1p=r(estimate) in 2
		replace pt1p_95u=r(estimate)+1.96*r(se) in 2
		replace pt1p_95l=r(estimate)-1.96*r(se) in 2
	lincom dwholesale_p+L1.dwholesale_p+L2.dwholesale_p
		replace pt1p=r(estimate) in 3
		replace pt1p_95u=r(estimate)+1.96*r(se) in 3
		replace pt1p_95l=r(estimate)-1.96*r(se) in 3
	lincom dwholesale_p+L1.dwholesale_p+L2.dwholesale_p+L3.dwholesale_p
		replace pt1p=r(estimate) in 4
		replace pt1p_95u=r(estimate)+1.96*r(se) in 4
		replace pt1p_95l=r(estimate)-1.96*r(se) in 4
	lincom dwholesale_p+L1.dwholesale_p+L2.dwholesale_p+L3.dwholesale_p+L4.dwholesale_p
		replace pt1p=r(estimate) in 5
		replace pt1p_95u=r(estimate)+1.96*r(se) in 5
		replace pt1p_95l=r(estimate)-1.96*r(se) in 5
	lincom dwholesale_p+L1.dwholesale_p+L2.dwholesale_p+L3.dwholesale_p+L4.dwholesale_p+ ///
		   L5.dwholesale_p
		replace pt1p=r(estimate) in 6
		replace pt1p_95u=r(estimate)+1.96*r(se) in 6
		replace pt1p_95l=r(estimate)-1.96*r(se) in 6			
	lincom dwholesale_p+L1.dwholesale_p+L2.dwholesale_p+L3.dwholesale_p+L4.dwholesale_p+ ///
		   L5.dwholesale_p+L6.dwholesale_p
		replace pt1p=r(estimate) in 7
		replace pt1p_95u=r(estimate)+1.96*r(se) in 7
		replace pt1p_95l=r(estimate)-1.96*r(se) in 7	
	lincom dwholesale_p+L1.dwholesale_p+L2.dwholesale_p+L3.dwholesale_p+L4.dwholesale_p+ ///
		   L5.dwholesale_p+L6.dwholesale_p+L7.dwholesale_p
		replace pt1p=r(estimate) in 8
		replace pt1p_95u=r(estimate)+1.96*r(se) in 8
		replace pt1p_95l=r(estimate)-1.96*r(se) in 8			
	lincom dwholesale_p+L1.dwholesale_p+L2.dwholesale_p+L3.dwholesale_p+L4.dwholesale_p+ ///
		   L5.dwholesale_p+L6.dwholesale_p+L7.dwholesale_p+L8.dwholesale_p
		replace pt1p=r(estimate) in 9
		replace pt1p_95u=r(estimate)+1.96*r(se) in 9
		replace pt1p_95l=r(estimate)-1.96*r(se) in 9			
	lincom dwholesale_p+L1.dwholesale_p+L2.dwholesale_p+L3.dwholesale_p+L4.dwholesale_p+ ///
		   L5.dwholesale_p+L6.dwholesale_p+L7.dwholesale_p+L8.dwholesale_p+L9.dwholesale_p
		replace pt1p=r(estimate) in 10
		replace pt1p_95u=r(estimate)+1.96*r(se) in 10
		replace pt1p_95l=r(estimate)-1.96*r(se) in 10			   
	lincom dwholesale_p+L1.dwholesale_p+L2.dwholesale_p+L3.dwholesale_p+L4.dwholesale_p+ ///
		   L5.dwholesale_p+L6.dwholesale_p+L7.dwholesale_p+L8.dwholesale_p+L9.dwholesale_p+ ///
		   L10.dwholesale_p
		replace pt1p=r(estimate) in 11
		replace pt1p_95u=r(estimate)+1.96*r(se) in 11
		replace pt1p_95l=r(estimate)-1.96*r(se) in 11			   
	lincom dwholesale_p+L1.dwholesale_p+L2.dwholesale_p+L3.dwholesale_p+L4.dwholesale_p+ ///
		   L5.dwholesale_p+L6.dwholesale_p+L7.dwholesale_p+L8.dwholesale_p+L9.dwholesale_p+ ///
		   L10.dwholesale_p+L11.dwholesale_p
		replace pt1p=r(estimate) in 12
		replace pt1p_95u=r(estimate)+1.96*r(se) in 12
		replace pt1p_95l=r(estimate)-1.96*r(se) in 12	
	lincom dwholesale_p+L1.dwholesale_p+L2.dwholesale_p+L3.dwholesale_p+L4.dwholesale_p+ ///
		   L5.dwholesale_p+L6.dwholesale_p+L7.dwholesale_p+L8.dwholesale_p+L9.dwholesale_p+ ///
		   L10.dwholesale_p+L11.dwholesale_p+L12.dwholesale_p
		replace pt1p=r(estimate) in 13
		replace pt1p_95u=r(estimate)+1.96*r(se) in 13
		replace pt1p_95l=r(estimate)-1.96*r(se) in 13	
	lincom dwholesale_p+L1.dwholesale_p+L2.dwholesale_p+L3.dwholesale_p+L4.dwholesale_p+ ///
		   L5.dwholesale_p+L6.dwholesale_p+L7.dwholesale_p+L8.dwholesale_p+L9.dwholesale_p+ ///
		   L10.dwholesale_p+L11.dwholesale_p+L12.dwholesale_p+L13.dwholesale_p
		replace pt1p=r(estimate) in 14
		replace pt1p_95u=r(estimate)+1.96*r(se) in 14
		replace pt1p_95l=r(estimate)-1.96*r(se) in 14	
	lincom dwholesale_p+L1.dwholesale_p+L2.dwholesale_p+L3.dwholesale_p+L4.dwholesale_p+ ///
		   L5.dwholesale_p+L6.dwholesale_p+L7.dwholesale_p+L8.dwholesale_p+L9.dwholesale_p+ ///
		   L10.dwholesale_p+L11.dwholesale_p+L12.dwholesale_p+L13.dwholesale_p+L14.dwholesale_p
		replace pt1p=r(estimate) in 15
		replace pt1p_95u=r(estimate)+1.96*r(se) in 15
		replace pt1p_95l=r(estimate)-1.96*r(se) in 15			
	lincom dwholesale_p+L1.dwholesale_p+L2.dwholesale_p+L3.dwholesale_p+L4.dwholesale_p+ ///
		   L5.dwholesale_p+L6.dwholesale_p+L7.dwholesale_p+L8.dwholesale_p+L9.dwholesale_p+ ///
		   L10.dwholesale_p+L11.dwholesale_p+L12.dwholesale_p+L13.dwholesale_p+L14.dwholesale_p+ ///
		   L15.dwholesale_p
		replace pt1p=r(estimate) in 16
		replace pt1p_95u=r(estimate)+1.96*r(se) in 16
		replace pt1p_95l=r(estimate)-1.96*r(se) in 16					
	lincom dwholesale_p+L1.dwholesale_p+L2.dwholesale_p+L3.dwholesale_p+L4.dwholesale_p+ ///
		   L5.dwholesale_p+L6.dwholesale_p+L7.dwholesale_p+L8.dwholesale_p+L9.dwholesale_p+ ///
		   L10.dwholesale_p+L11.dwholesale_p+L12.dwholesale_p+L13.dwholesale_p+L14.dwholesale_p+ ///
		   L15.dwholesale_p+L16.dwholesale_p
		replace pt1p=r(estimate) in 17
		replace pt1p_95u=r(estimate)+1.96*r(se) in 17
		replace pt1p_95l=r(estimate)-1.96*r(se) in 17			
	lincom dwholesale_p+L1.dwholesale_p+L2.dwholesale_p+L3.dwholesale_p+L4.dwholesale_p+ ///
		   L5.dwholesale_p+L6.dwholesale_p+L7.dwholesale_p+L8.dwholesale_p+L9.dwholesale_p+ ///
		   L10.dwholesale_p+L11.dwholesale_p+L12.dwholesale_p+L13.dwholesale_p+L14.dwholesale_p+ ///
		   L15.dwholesale_p+L16.dwholesale_p+L17.dwholesale_p
		replace pt1p=r(estimate) in 18
		replace pt1p_95u=r(estimate)+1.96*r(se) in 18
		replace pt1p_95l=r(estimate)-1.96*r(se) in 18		
	lincom dwholesale_p+L1.dwholesale_p+L2.dwholesale_p+L3.dwholesale_p+L4.dwholesale_p+ ///
		   L5.dwholesale_p+L6.dwholesale_p+L7.dwholesale_p+L8.dwholesale_p+L9.dwholesale_p+ ///
		   L10.dwholesale_p+L11.dwholesale_p+L12.dwholesale_p+L13.dwholesale_p+L14.dwholesale_p+ ///
		   L15.dwholesale_p+L16.dwholesale_p+L17.dwholesale_p+L18.dwholesale_p
		replace pt1p=r(estimate) in 19
		replace pt1p_95u=r(estimate)+1.96*r(se) in 19
		replace pt1p_95l=r(estimate)-1.96*r(se) in 19		
	lincom dwholesale_p+L1.dwholesale_p+L2.dwholesale_p+L3.dwholesale_p+L4.dwholesale_p+ ///
		   L5.dwholesale_p+L6.dwholesale_p+L7.dwholesale_p+L8.dwholesale_p+L9.dwholesale_p+ ///
		   L10.dwholesale_p+L11.dwholesale_p+L12.dwholesale_p+L13.dwholesale_p+L14.dwholesale_p+ ///
		   L15.dwholesale_p+L16.dwholesale_p+L17.dwholesale_p+L18.dwholesale_p+L19.dwholesale_p
		replace pt1p=r(estimate) in 20
		replace pt1p_95u=r(estimate)+1.96*r(se) in 20
		replace pt1p_95l=r(estimate)-1.96*r(se) in 20
	lincom dwholesale_p+L1.dwholesale_p+L2.dwholesale_p+L3.dwholesale_p+L4.dwholesale_p+ ///
		   L5.dwholesale_p+L6.dwholesale_p+L7.dwholesale_p+L8.dwholesale_p+L9.dwholesale_p+ ///
		   L10.dwholesale_p+L11.dwholesale_p+L12.dwholesale_p+L13.dwholesale_p+L14.dwholesale_p+ ///
		   L15.dwholesale_p+L16.dwholesale_p+L17.dwholesale_p+L18.dwholesale_p+L19.dwholesale_p+ ///
		   L20.dwholesale_p
		replace pt1p=r(estimate) in 21
		replace pt1p_95u=r(estimate)+1.96*r(se) in 21
		replace pt1p_95l=r(estimate)-1.96*r(se) in 21		
	lincom dwholesale_p+L1.dwholesale_p+L2.dwholesale_p+L3.dwholesale_p+L4.dwholesale_p+ ///
		   L5.dwholesale_p+L6.dwholesale_p+L7.dwholesale_p+L8.dwholesale_p+L9.dwholesale_p+ ///
		   L10.dwholesale_p+L11.dwholesale_p+L12.dwholesale_p+L13.dwholesale_p+L14.dwholesale_p+ ///
		   L15.dwholesale_p+L16.dwholesale_p+L17.dwholesale_p+L18.dwholesale_p+L19.dwholesale_p+ ///
		   L20.dwholesale_p+L21.dwholesale_p
		replace pt1p=r(estimate) in 22
		replace pt1p_95u=r(estimate)+1.96*r(se) in 22
		replace pt1p_95l=r(estimate)-1.96*r(se) in 22			
	lincom dwholesale_p+L1.dwholesale_p+L2.dwholesale_p+L3.dwholesale_p+L4.dwholesale_p+ ///
		   L5.dwholesale_p+L6.dwholesale_p+L7.dwholesale_p+L8.dwholesale_p+L9.dwholesale_p+ ///
		   L10.dwholesale_p+L11.dwholesale_p+L12.dwholesale_p+L13.dwholesale_p+L14.dwholesale_p+ ///
		   L15.dwholesale_p+L16.dwholesale_p+L17.dwholesale_p+L18.dwholesale_p+L19.dwholesale_p+ ///
		   L20.dwholesale_p+L21.dwholesale_p+L22.dwholesale_p
		replace pt1p=r(estimate) in 23
		replace pt1p_95u=r(estimate)+1.96*r(se) in 23
		replace pt1p_95l=r(estimate)-1.96*r(se) in 23	
	lincom dwholesale_p+L1.dwholesale_p+L2.dwholesale_p+L3.dwholesale_p+L4.dwholesale_p+ ///
		   L5.dwholesale_p+L6.dwholesale_p+L7.dwholesale_p+L8.dwholesale_p+L9.dwholesale_p+ ///
		   L10.dwholesale_p+L11.dwholesale_p+L12.dwholesale_p+L13.dwholesale_p+L14.dwholesale_p+ ///
		   L15.dwholesale_p+L16.dwholesale_p+L17.dwholesale_p+L18.dwholesale_p+L19.dwholesale_p+ ///
		   L20.dwholesale_p+L21.dwholesale_p+L22.dwholesale_p+L23.dwholesale_p
		replace pt1p=r(estimate) in 24
		replace pt1p_95u=r(estimate)+1.96*r(se) in 24
		replace pt1p_95l=r(estimate)-1.96*r(se) in 24	
	lincom dwholesale_p+L1.dwholesale_p+L2.dwholesale_p+L3.dwholesale_p+L4.dwholesale_p+ ///
		   L5.dwholesale_p+L6.dwholesale_p+L7.dwholesale_p+L8.dwholesale_p+L9.dwholesale_p+ ///
		   L10.dwholesale_p+L11.dwholesale_p+L12.dwholesale_p+L13.dwholesale_p+L14.dwholesale_p+ ///
		   L15.dwholesale_p+L16.dwholesale_p+L17.dwholesale_p+L18.dwholesale_p+L19.dwholesale_p+ ///
		   L20.dwholesale_p+L21.dwholesale_p+L22.dwholesale_p+L23.dwholesale_p+L24.dwholesale_p
		replace pt1p=r(estimate) in 25
		replace pt1p_95u=r(estimate)+1.96*r(se) in 25
		replace pt1p_95l=r(estimate)-1.96*r(se) in 25
	lincom dwholesale_p+L1.dwholesale_p+L2.dwholesale_p+L3.dwholesale_p+L4.dwholesale_p+ ///
		   L5.dwholesale_p+L6.dwholesale_p+L7.dwholesale_p+L8.dwholesale_p+L9.dwholesale_p+ ///
		   L10.dwholesale_p+L11.dwholesale_p+L12.dwholesale_p+L13.dwholesale_p+L14.dwholesale_p+ ///
		   L15.dwholesale_p+L16.dwholesale_p+L17.dwholesale_p+L18.dwholesale_p+L19.dwholesale_p+ ///
		   L20.dwholesale_p+L21.dwholesale_p+L22.dwholesale_p+L23.dwholesale_p+L24.dwholesale_p+ ///
		   L25.dwholesale_p
		replace pt1p=r(estimate) in 26
		replace pt1p_95u=r(estimate)+1.96*r(se) in 26
		replace pt1p_95l=r(estimate)-1.96*r(se) in 26		
	lincom dwholesale_p+L1.dwholesale_p+L2.dwholesale_p+L3.dwholesale_p+L4.dwholesale_p+ ///
		   L5.dwholesale_p+L6.dwholesale_p+L7.dwholesale_p+L8.dwholesale_p+L9.dwholesale_p+ ///
		   L10.dwholesale_p+L11.dwholesale_p+L12.dwholesale_p+L13.dwholesale_p+L14.dwholesale_p+ ///
		   L15.dwholesale_p+L16.dwholesale_p+L17.dwholesale_p+L18.dwholesale_p+L19.dwholesale_p+ ///
		   L20.dwholesale_p+L21.dwholesale_p+L22.dwholesale_p+L23.dwholesale_p+L24.dwholesale_p+ ///
		   L25.dwholesale_p+L26.dwholesale_p
		replace pt1p=r(estimate) in 27
		replace pt1p_95u=r(estimate)+1.96*r(se) in 27
		replace pt1p_95l=r(estimate)-1.96*r(se) in 27	
	lincom dwholesale_p+L1.dwholesale_p+L2.dwholesale_p+L3.dwholesale_p+L4.dwholesale_p+ ///
		   L5.dwholesale_p+L6.dwholesale_p+L7.dwholesale_p+L8.dwholesale_p+L9.dwholesale_p+ ///
		   L10.dwholesale_p+L11.dwholesale_p+L12.dwholesale_p+L13.dwholesale_p+L14.dwholesale_p+ ///
		   L15.dwholesale_p+L16.dwholesale_p+L17.dwholesale_p+L18.dwholesale_p+L19.dwholesale_p+ ///
		   L20.dwholesale_p+L21.dwholesale_p+L22.dwholesale_p+L23.dwholesale_p+L24.dwholesale_p+ ///
		   L25.dwholesale_p+L26.dwholesale_p+L27.dwholesale_p
		replace pt1p=r(estimate) in 28
		replace pt1p_95u=r(estimate)+1.96*r(se) in 28
		replace pt1p_95l=r(estimate)-1.96*r(se) in 28			
	lincom dwholesale_p+L1.dwholesale_p+L2.dwholesale_p+L3.dwholesale_p+L4.dwholesale_p+ ///
		   L5.dwholesale_p+L6.dwholesale_p+L7.dwholesale_p+L8.dwholesale_p+L9.dwholesale_p+ ///
		   L10.dwholesale_p+L11.dwholesale_p+L12.dwholesale_p+L13.dwholesale_p+L14.dwholesale_p+ ///
		   L15.dwholesale_p+L16.dwholesale_p+L17.dwholesale_p+L18.dwholesale_p+L19.dwholesale_p+ ///
		   L20.dwholesale_p+L21.dwholesale_p+L22.dwholesale_p+L23.dwholesale_p+L24.dwholesale_p+ ///
		   L25.dwholesale_p+L26.dwholesale_p+L27.dwholesale_p+L28.dwholesale_p
		replace pt1p=r(estimate) in 29
		replace pt1p_95u=r(estimate)+1.96*r(se) in 29
		replace pt1p_95l=r(estimate)-1.96*r(se) in 29
	lincom dwholesale_p+L1.dwholesale_p+L2.dwholesale_p+L3.dwholesale_p+L4.dwholesale_p+ ///
		   L5.dwholesale_p+L6.dwholesale_p+L7.dwholesale_p+L8.dwholesale_p+L9.dwholesale_p+ ///
		   L10.dwholesale_p+L11.dwholesale_p+L12.dwholesale_p+L13.dwholesale_p+L14.dwholesale_p+ ///
		   L15.dwholesale_p+L16.dwholesale_p+L17.dwholesale_p+L18.dwholesale_p+L19.dwholesale_p+ ///
		   L20.dwholesale_p+L21.dwholesale_p+L22.dwholesale_p+L23.dwholesale_p+L24.dwholesale_p+ ///
		   L25.dwholesale_p+L26.dwholesale_p+L27.dwholesale_p+L28.dwholesale_p+L29.dwholesale_p
		replace pt1p=r(estimate) in 30
		replace pt1p_95u=r(estimate)+1.96*r(se) in 30
		replace pt1p_95l=r(estimate)-1.96*r(se) in 30
	lincom dwholesale_p+L1.dwholesale_p+L2.dwholesale_p+L3.dwholesale_p+L4.dwholesale_p+ ///
		   L5.dwholesale_p+L6.dwholesale_p+L7.dwholesale_p+L8.dwholesale_p+L9.dwholesale_p+ ///
		   L10.dwholesale_p+L11.dwholesale_p+L12.dwholesale_p+L13.dwholesale_p+L14.dwholesale_p+ ///
		   L15.dwholesale_p+L16.dwholesale_p+L17.dwholesale_p+L18.dwholesale_p+L19.dwholesale_p+ ///
		   L20.dwholesale_p+L21.dwholesale_p+L22.dwholesale_p+L23.dwholesale_p+L24.dwholesale_p+ ///
		   L25.dwholesale_p+L26.dwholesale_p+L27.dwholesale_p+L28.dwholesale_p+L29.dwholesale_p+ ///
		   L30.dwholesale_p
		replace pt1p=r(estimate) in 31
		replace pt1p_95u=r(estimate)+1.96*r(se) in 31
		replace pt1p_95l=r(estimate)-1.96*r(se) in 31
*
*Negative Shocks
	replace pt1n=_b[dwholesale_n] in 1
		replace pt1n_95l=_b[dwholesale_n]- 1.96*_se[dwholesale_n] in 1
		replace pt1n_95u=_b[dwholesale_n]+ 1.96*_se[dwholesale_n] in 1
	lincom dwholesale_n+L1.dwholesale_n
		replace pt1n=r(estimate) in 2
		replace pt1n_95u=r(estimate)+1.96*r(se) in 2
		replace pt1n_95l=r(estimate)-1.96*r(se) in 2
	lincom dwholesale_n+L1.dwholesale_n+L2.dwholesale_n
		replace pt1n=r(estimate) in 3
		replace pt1n_95u=r(estimate)+1.96*r(se) in 3
		replace pt1n_95l=r(estimate)-1.96*r(se) in 3
	lincom dwholesale_n+L1.dwholesale_n+L2.dwholesale_n+L3.dwholesale_n
		replace pt1n=r(estimate) in 4
		replace pt1n_95u=r(estimate)+1.96*r(se) in 4
		replace pt1n_95l=r(estimate)-1.96*r(se) in 4
	lincom dwholesale_n+L1.dwholesale_n+L2.dwholesale_n+L3.dwholesale_n+L4.dwholesale_n
		replace pt1n=r(estimate) in 5
		replace pt1n_95u=r(estimate)+1.96*r(se) in 5
		replace pt1n_95l=r(estimate)-1.96*r(se) in 5
	lincom dwholesale_n+L1.dwholesale_n+L2.dwholesale_n+L3.dwholesale_n+L4.dwholesale_n+ ///
		   L5.dwholesale_n
		replace pt1n=r(estimate) in 6
		replace pt1n_95u=r(estimate)+1.96*r(se) in 6
		replace pt1n_95l=r(estimate)-1.96*r(se) in 6			
	lincom dwholesale_n+L1.dwholesale_n+L2.dwholesale_n+L3.dwholesale_n+L4.dwholesale_n+ ///
		   L5.dwholesale_n+L6.dwholesale_n
		replace pt1n=r(estimate) in 7
		replace pt1n_95u=r(estimate)+1.96*r(se) in 7
		replace pt1n_95l=r(estimate)-1.96*r(se) in 7	
	lincom dwholesale_n+L1.dwholesale_n+L2.dwholesale_n+L3.dwholesale_n+L4.dwholesale_n+ ///
		   L5.dwholesale_n+L6.dwholesale_n+L7.dwholesale_n
		replace pt1n=r(estimate) in 8
		replace pt1n_95u=r(estimate)+1.96*r(se) in 8
		replace pt1n_95l=r(estimate)-1.96*r(se) in 8			
	lincom dwholesale_n+L1.dwholesale_n+L2.dwholesale_n+L3.dwholesale_n+L4.dwholesale_n+ ///
		   L5.dwholesale_n+L6.dwholesale_n+L7.dwholesale_n+L8.dwholesale_n
		replace pt1n=r(estimate) in 9
		replace pt1n_95u=r(estimate)+1.96*r(se) in 9
		replace pt1n_95l=r(estimate)-1.96*r(se) in 9			
	lincom dwholesale_n+L1.dwholesale_n+L2.dwholesale_n+L3.dwholesale_n+L4.dwholesale_n+ ///
		   L5.dwholesale_n+L6.dwholesale_n+L7.dwholesale_n+L8.dwholesale_n+L9.dwholesale_n
		replace pt1n=r(estimate) in 10
		replace pt1n_95u=r(estimate)+1.96*r(se) in 10
		replace pt1n_95l=r(estimate)-1.96*r(se) in 10			   
	lincom dwholesale_n+L1.dwholesale_n+L2.dwholesale_n+L3.dwholesale_n+L4.dwholesale_n+ ///
		   L5.dwholesale_n+L6.dwholesale_n+L7.dwholesale_n+L8.dwholesale_n+L9.dwholesale_n+ ///
		   L10.dwholesale_n
		replace pt1n=r(estimate) in 11
		replace pt1n_95u=r(estimate)+1.96*r(se) in 11
		replace pt1n_95l=r(estimate)-1.96*r(se) in 11			   
	lincom dwholesale_n+L1.dwholesale_n+L2.dwholesale_n+L3.dwholesale_n+L4.dwholesale_n+ ///
		   L5.dwholesale_n+L6.dwholesale_n+L7.dwholesale_n+L8.dwholesale_n+L9.dwholesale_n+ ///
		   L10.dwholesale_n+L11.dwholesale_n
		replace pt1n=r(estimate) in 12
		replace pt1n_95u=r(estimate)+1.96*r(se) in 12
		replace pt1n_95l=r(estimate)-1.96*r(se) in 12	
	lincom dwholesale_n+L1.dwholesale_n+L2.dwholesale_n+L3.dwholesale_n+L4.dwholesale_n+ ///
		   L5.dwholesale_n+L6.dwholesale_n+L7.dwholesale_n+L8.dwholesale_n+L9.dwholesale_n+ ///
		   L10.dwholesale_n+L11.dwholesale_n+L12.dwholesale_n
		replace pt1n=r(estimate) in 13
		replace pt1n_95u=r(estimate)+1.96*r(se) in 13
		replace pt1n_95l=r(estimate)-1.96*r(se) in 13	
	lincom dwholesale_n+L1.dwholesale_n+L2.dwholesale_n+L3.dwholesale_n+L4.dwholesale_n+ ///
		   L5.dwholesale_n+L6.dwholesale_n+L7.dwholesale_n+L8.dwholesale_n+L9.dwholesale_n+ ///
		   L10.dwholesale_n+L11.dwholesale_n+L12.dwholesale_n+L13.dwholesale_n
		replace pt1n=r(estimate) in 14
		replace pt1n_95u=r(estimate)+1.96*r(se) in 14
		replace pt1n_95l=r(estimate)-1.96*r(se) in 14	
	lincom dwholesale_n+L1.dwholesale_n+L2.dwholesale_n+L3.dwholesale_n+L4.dwholesale_n+ ///
		   L5.dwholesale_n+L6.dwholesale_n+L7.dwholesale_n+L8.dwholesale_n+L9.dwholesale_n+ ///
		   L10.dwholesale_n+L11.dwholesale_n+L12.dwholesale_n+L13.dwholesale_n+L14.dwholesale_n
		replace pt1n=r(estimate) in 15
		replace pt1n_95u=r(estimate)+1.96*r(se) in 15
		replace pt1n_95l=r(estimate)-1.96*r(se) in 15			
	lincom dwholesale_n+L1.dwholesale_n+L2.dwholesale_n+L3.dwholesale_n+L4.dwholesale_n+ ///
		   L5.dwholesale_n+L6.dwholesale_n+L7.dwholesale_n+L8.dwholesale_n+L9.dwholesale_n+ ///
		   L10.dwholesale_n+L11.dwholesale_n+L12.dwholesale_n+L13.dwholesale_n+L14.dwholesale_n+ ///
		   L15.dwholesale_n
		replace pt1n=r(estimate) in 16
		replace pt1n_95u=r(estimate)+1.96*r(se) in 16
		replace pt1n_95l=r(estimate)-1.96*r(se) in 16					
	lincom dwholesale_n+L1.dwholesale_n+L2.dwholesale_n+L3.dwholesale_n+L4.dwholesale_n+ ///
		   L5.dwholesale_n+L6.dwholesale_n+L7.dwholesale_n+L8.dwholesale_n+L9.dwholesale_n+ ///
		   L10.dwholesale_n+L11.dwholesale_n+L12.dwholesale_n+L13.dwholesale_n+L14.dwholesale_n+ ///
		   L15.dwholesale_n+L16.dwholesale_n
		replace pt1n=r(estimate) in 17
		replace pt1n_95u=r(estimate)+1.96*r(se) in 17
		replace pt1n_95l=r(estimate)-1.96*r(se) in 17			
	lincom dwholesale_n+L1.dwholesale_n+L2.dwholesale_n+L3.dwholesale_n+L4.dwholesale_n+ ///
		   L5.dwholesale_n+L6.dwholesale_n+L7.dwholesale_n+L8.dwholesale_n+L9.dwholesale_n+ ///
		   L10.dwholesale_n+L11.dwholesale_n+L12.dwholesale_n+L13.dwholesale_n+L14.dwholesale_n+ ///
		   L15.dwholesale_n+L16.dwholesale_n+L17.dwholesale_n
		replace pt1n=r(estimate) in 18
		replace pt1n_95u=r(estimate)+1.96*r(se) in 18
		replace pt1n_95l=r(estimate)-1.96*r(se) in 18		
	lincom dwholesale_n+L1.dwholesale_n+L2.dwholesale_n+L3.dwholesale_n+L4.dwholesale_n+ ///
		   L5.dwholesale_n+L6.dwholesale_n+L7.dwholesale_n+L8.dwholesale_n+L9.dwholesale_n+ ///
		   L10.dwholesale_n+L11.dwholesale_n+L12.dwholesale_n+L13.dwholesale_n+L14.dwholesale_n+ ///
		   L15.dwholesale_n+L16.dwholesale_n+L17.dwholesale_n+L18.dwholesale_n
		replace pt1n=r(estimate) in 19
		replace pt1n_95u=r(estimate)+1.96*r(se) in 19
		replace pt1n_95l=r(estimate)-1.96*r(se) in 19		
	lincom dwholesale_n+L1.dwholesale_n+L2.dwholesale_n+L3.dwholesale_n+L4.dwholesale_n+ ///
		   L5.dwholesale_n+L6.dwholesale_n+L7.dwholesale_n+L8.dwholesale_n+L9.dwholesale_n+ ///
		   L10.dwholesale_n+L11.dwholesale_n+L12.dwholesale_n+L13.dwholesale_n+L14.dwholesale_n+ ///
		   L15.dwholesale_n+L16.dwholesale_n+L17.dwholesale_n+L18.dwholesale_n+L19.dwholesale_n
		replace pt1n=r(estimate) in 20
		replace pt1n_95u=r(estimate)+1.96*r(se) in 20
		replace pt1n_95l=r(estimate)-1.96*r(se) in 20
	lincom dwholesale_n+L1.dwholesale_n+L2.dwholesale_n+L3.dwholesale_n+L4.dwholesale_n+ ///
		   L5.dwholesale_n+L6.dwholesale_n+L7.dwholesale_n+L8.dwholesale_n+L9.dwholesale_n+ ///
		   L10.dwholesale_n+L11.dwholesale_n+L12.dwholesale_n+L13.dwholesale_n+L14.dwholesale_n+ ///
		   L15.dwholesale_n+L16.dwholesale_n+L17.dwholesale_n+L18.dwholesale_n+L19.dwholesale_n+ ///
		   L20.dwholesale_n
		replace pt1n=r(estimate) in 21
		replace pt1n_95u=r(estimate)+1.96*r(se) in 21
		replace pt1n_95l=r(estimate)-1.96*r(se) in 21		
	lincom dwholesale_n+L1.dwholesale_n+L2.dwholesale_n+L3.dwholesale_n+L4.dwholesale_n+ ///
		   L5.dwholesale_n+L6.dwholesale_n+L7.dwholesale_n+L8.dwholesale_n+L9.dwholesale_n+ ///
		   L10.dwholesale_n+L11.dwholesale_n+L12.dwholesale_n+L13.dwholesale_n+L14.dwholesale_n+ ///
		   L15.dwholesale_n+L16.dwholesale_n+L17.dwholesale_n+L18.dwholesale_n+L19.dwholesale_n+ ///
		   L20.dwholesale_n+L21.dwholesale_n
		replace pt1n=r(estimate) in 22
		replace pt1n_95u=r(estimate)+1.96*r(se) in 22
		replace pt1n_95l=r(estimate)-1.96*r(se) in 22			
	lincom dwholesale_n+L1.dwholesale_n+L2.dwholesale_n+L3.dwholesale_n+L4.dwholesale_n+ ///
		   L5.dwholesale_n+L6.dwholesale_n+L7.dwholesale_n+L8.dwholesale_n+L9.dwholesale_n+ ///
		   L10.dwholesale_n+L11.dwholesale_n+L12.dwholesale_n+L13.dwholesale_n+L14.dwholesale_n+ ///
		   L15.dwholesale_n+L16.dwholesale_n+L17.dwholesale_n+L18.dwholesale_n+L19.dwholesale_n+ ///
		   L20.dwholesale_n+L21.dwholesale_n+L22.dwholesale_n
		replace pt1n=r(estimate) in 23
		replace pt1n_95u=r(estimate)+1.96*r(se) in 23
		replace pt1n_95l=r(estimate)-1.96*r(se) in 23	
	lincom dwholesale_n+L1.dwholesale_n+L2.dwholesale_n+L3.dwholesale_n+L4.dwholesale_n+ ///
		   L5.dwholesale_n+L6.dwholesale_n+L7.dwholesale_n+L8.dwholesale_n+L9.dwholesale_n+ ///
		   L10.dwholesale_n+L11.dwholesale_n+L12.dwholesale_n+L13.dwholesale_n+L14.dwholesale_n+ ///
		   L15.dwholesale_n+L16.dwholesale_n+L17.dwholesale_n+L18.dwholesale_n+L19.dwholesale_n+ ///
		   L20.dwholesale_n+L21.dwholesale_n+L22.dwholesale_n+L23.dwholesale_n
		replace pt1n=r(estimate) in 24
		replace pt1n_95u=r(estimate)+1.96*r(se) in 24
		replace pt1n_95l=r(estimate)-1.96*r(se) in 24	
	lincom dwholesale_n+L1.dwholesale_n+L2.dwholesale_n+L3.dwholesale_n+L4.dwholesale_n+ ///
		   L5.dwholesale_n+L6.dwholesale_n+L7.dwholesale_n+L8.dwholesale_n+L9.dwholesale_n+ ///
		   L10.dwholesale_n+L11.dwholesale_n+L12.dwholesale_n+L13.dwholesale_n+L14.dwholesale_n+ ///
		   L15.dwholesale_n+L16.dwholesale_n+L17.dwholesale_n+L18.dwholesale_n+L19.dwholesale_n+ ///
		   L20.dwholesale_n+L21.dwholesale_n+L22.dwholesale_n+L23.dwholesale_n+L24.dwholesale_n
		replace pt1n=r(estimate) in 25
		replace pt1n_95u=r(estimate)+1.96*r(se) in 25
		replace pt1n_95l=r(estimate)-1.96*r(se) in 25
	lincom dwholesale_n+L1.dwholesale_n+L2.dwholesale_n+L3.dwholesale_n+L4.dwholesale_n+ ///
		   L5.dwholesale_n+L6.dwholesale_n+L7.dwholesale_n+L8.dwholesale_n+L9.dwholesale_n+ ///
		   L10.dwholesale_n+L11.dwholesale_n+L12.dwholesale_n+L13.dwholesale_n+L14.dwholesale_n+ ///
		   L15.dwholesale_n+L16.dwholesale_n+L17.dwholesale_n+L18.dwholesale_n+L19.dwholesale_n+ ///
		   L20.dwholesale_n+L21.dwholesale_n+L22.dwholesale_n+L23.dwholesale_n+L24.dwholesale_n+ ///
		   L25.dwholesale_n
		replace pt1n=r(estimate) in 26
		replace pt1n_95u=r(estimate)+1.96*r(se) in 26
		replace pt1n_95l=r(estimate)-1.96*r(se) in 26		
	lincom dwholesale_n+L1.dwholesale_n+L2.dwholesale_n+L3.dwholesale_n+L4.dwholesale_n+ ///
		   L5.dwholesale_n+L6.dwholesale_n+L7.dwholesale_n+L8.dwholesale_n+L9.dwholesale_n+ ///
		   L10.dwholesale_n+L11.dwholesale_n+L12.dwholesale_n+L13.dwholesale_n+L14.dwholesale_n+ ///
		   L15.dwholesale_n+L16.dwholesale_n+L17.dwholesale_n+L18.dwholesale_n+L19.dwholesale_n+ ///
		   L20.dwholesale_n+L21.dwholesale_n+L22.dwholesale_n+L23.dwholesale_n+L24.dwholesale_n+ ///
		   L25.dwholesale_n+L26.dwholesale_n
		replace pt1n=r(estimate) in 27
		replace pt1n_95u=r(estimate)+1.96*r(se) in 27
		replace pt1n_95l=r(estimate)-1.96*r(se) in 27	
	lincom dwholesale_n+L1.dwholesale_n+L2.dwholesale_n+L3.dwholesale_n+L4.dwholesale_n+ ///
		   L5.dwholesale_n+L6.dwholesale_n+L7.dwholesale_n+L8.dwholesale_n+L9.dwholesale_n+ ///
		   L10.dwholesale_n+L11.dwholesale_n+L12.dwholesale_n+L13.dwholesale_n+L14.dwholesale_n+ ///
		   L15.dwholesale_n+L16.dwholesale_n+L17.dwholesale_n+L18.dwholesale_n+L19.dwholesale_n+ ///
		   L20.dwholesale_n+L21.dwholesale_n+L22.dwholesale_n+L23.dwholesale_n+L24.dwholesale_n+ ///
		   L25.dwholesale_n+L26.dwholesale_n+L27.dwholesale_n
		replace pt1n=r(estimate) in 28
		replace pt1n_95u=r(estimate)+1.96*r(se) in 28
		replace pt1n_95l=r(estimate)-1.96*r(se) in 28			
	lincom dwholesale_n+L1.dwholesale_n+L2.dwholesale_n+L3.dwholesale_n+L4.dwholesale_n+ ///
		   L5.dwholesale_n+L6.dwholesale_n+L7.dwholesale_n+L8.dwholesale_n+L9.dwholesale_n+ ///
		   L10.dwholesale_n+L11.dwholesale_n+L12.dwholesale_n+L13.dwholesale_n+L14.dwholesale_n+ ///
		   L15.dwholesale_n+L16.dwholesale_n+L17.dwholesale_n+L18.dwholesale_n+L19.dwholesale_n+ ///
		   L20.dwholesale_n+L21.dwholesale_n+L22.dwholesale_n+L23.dwholesale_n+L24.dwholesale_n+ ///
		   L25.dwholesale_n+L26.dwholesale_n+L27.dwholesale_n+L28.dwholesale_n
		replace pt1n=r(estimate) in 29
		replace pt1n_95u=r(estimate)+1.96*r(se) in 29
		replace pt1n_95l=r(estimate)-1.96*r(se) in 29
	lincom dwholesale_n+L1.dwholesale_n+L2.dwholesale_n+L3.dwholesale_n+L4.dwholesale_n+ ///
		   L5.dwholesale_n+L6.dwholesale_n+L7.dwholesale_n+L8.dwholesale_n+L9.dwholesale_n+ ///
		   L10.dwholesale_n+L11.dwholesale_n+L12.dwholesale_n+L13.dwholesale_n+L14.dwholesale_n+ ///
		   L15.dwholesale_n+L16.dwholesale_n+L17.dwholesale_n+L18.dwholesale_n+L19.dwholesale_n+ ///
		   L20.dwholesale_n+L21.dwholesale_n+L22.dwholesale_n+L23.dwholesale_n+L24.dwholesale_n+ ///
		   L25.dwholesale_n+L26.dwholesale_n+L27.dwholesale_n+L28.dwholesale_n+L29.dwholesale_n
		replace pt1n=r(estimate) in 30
		replace pt1n_95u=r(estimate)+1.96*r(se) in 30
		replace pt1n_95l=r(estimate)-1.96*r(se) in 30
	lincom dwholesale_n+L1.dwholesale_n+L2.dwholesale_n+L3.dwholesale_n+L4.dwholesale_n+ ///
		   L5.dwholesale_n+L6.dwholesale_n+L7.dwholesale_n+L8.dwholesale_n+L9.dwholesale_n+ ///
		   L10.dwholesale_n+L11.dwholesale_n+L12.dwholesale_n+L13.dwholesale_n+L14.dwholesale_n+ ///
		   L15.dwholesale_n+L16.dwholesale_n+L17.dwholesale_n+L18.dwholesale_n+L19.dwholesale_n+ ///
		   L20.dwholesale_n+L21.dwholesale_n+L22.dwholesale_n+L23.dwholesale_n+L24.dwholesale_n+ ///
		   L25.dwholesale_n+L26.dwholesale_n+L27.dwholesale_n+L28.dwholesale_n+L29.dwholesale_n+ ///
		   L30.dwholesale_n
		replace pt1n=r(estimate) in 31
		replace pt1n_95u=r(estimate)+1.96*r(se) in 31
		replace pt1n_95l=r(estimate)-1.96*r(se) in 31
*				
*Graph
twoway scatter pt1p event_d1, m(diamond) mc(edkblue) msize(medsmall) || /// 
	   rcap pt1p_95u pt1p_95l event_d1, lstyle(ci) lc(edkblue) || ///
	   rcap pt1n_95u pt1n_95l event_d2, lstyle(ci) lc(cranberry) || ///
	   scatter pt1n event_d2, m(triangle) mc(cranberry) msize(medsmall)  ///                 
	   graphregion(color(white)) bgcolor(white) ///
	   legend(order(1 "Positive" 4 "Negative" ) ///
	   rows(1) region(lcolor(white))) ///
	   xtitle("Days After $1/gallon Wholesale Cost Shock") ///  
	   ytitle(Pass Through ($/gal)) xlabel(0(3)30, nogrid)  ///
	   ylabel(0(0.5)1, nogrid)  ///
	   yline(0 0.5  1, lstyle(grid) lcolor(black*0.05))
graph export $figs/pt_all_asym_$outputdate.png, replace width(4000)
*
*
*****************
*PANEL (B): LANDFALL STATIONS, HURRICANE WINDOW 		
reghdfe d.retail l(0/30).dwholesale_p l(0/30).dwholesale_n temp temp2 if landfall_hur_sample==1, ///
		absorb(cons) cluster(county_FIPS)
*		
*Positive Shocks
	replace pt2p=_b[dwholesale_p] in 1
		replace pt2p_95l=_b[dwholesale_p]- 1.96*_se[dwholesale_p] in 1
		replace pt2p_95u=_b[dwholesale_p]+ 1.96*_se[dwholesale_p] in 1
	lincom dwholesale_p+L1.dwholesale_p
		replace pt2p=r(estimate) in 2
		replace pt2p_95u=r(estimate)+1.96*r(se) in 2
		replace pt2p_95l=r(estimate)-1.96*r(se) in 2
	lincom dwholesale_p+L1.dwholesale_p+L2.dwholesale_p
		replace pt2p=r(estimate) in 3
		replace pt2p_95u=r(estimate)+1.96*r(se) in 3
		replace pt2p_95l=r(estimate)-1.96*r(se) in 3
	lincom dwholesale_p+L1.dwholesale_p+L2.dwholesale_p+L3.dwholesale_p
		replace pt2p=r(estimate) in 4
		replace pt2p_95u=r(estimate)+1.96*r(se) in 4
		replace pt2p_95l=r(estimate)-1.96*r(se) in 4
	lincom dwholesale_p+L1.dwholesale_p+L2.dwholesale_p+L3.dwholesale_p+L4.dwholesale_p
		replace pt2p=r(estimate) in 5
		replace pt2p_95u=r(estimate)+1.96*r(se) in 5
		replace pt2p_95l=r(estimate)-1.96*r(se) in 5
	lincom dwholesale_p+L1.dwholesale_p+L2.dwholesale_p+L3.dwholesale_p+L4.dwholesale_p+ ///
		   L5.dwholesale_p
		replace pt2p=r(estimate) in 6
		replace pt2p_95u=r(estimate)+1.96*r(se) in 6
		replace pt2p_95l=r(estimate)-1.96*r(se) in 6			
	lincom dwholesale_p+L1.dwholesale_p+L2.dwholesale_p+L3.dwholesale_p+L4.dwholesale_p+ ///
		   L5.dwholesale_p+L6.dwholesale_p
		replace pt2p=r(estimate) in 7
		replace pt2p_95u=r(estimate)+1.96*r(se) in 7
		replace pt2p_95l=r(estimate)-1.96*r(se) in 7	
	lincom dwholesale_p+L1.dwholesale_p+L2.dwholesale_p+L3.dwholesale_p+L4.dwholesale_p+ ///
		   L5.dwholesale_p+L6.dwholesale_p+L7.dwholesale_p
		replace pt2p=r(estimate) in 8
		replace pt2p_95u=r(estimate)+1.96*r(se) in 8
		replace pt2p_95l=r(estimate)-1.96*r(se) in 8			
	lincom dwholesale_p+L1.dwholesale_p+L2.dwholesale_p+L3.dwholesale_p+L4.dwholesale_p+ ///
		   L5.dwholesale_p+L6.dwholesale_p+L7.dwholesale_p+L8.dwholesale_p
		replace pt2p=r(estimate) in 9
		replace pt2p_95u=r(estimate)+1.96*r(se) in 9
		replace pt2p_95l=r(estimate)-1.96*r(se) in 9			
	lincom dwholesale_p+L1.dwholesale_p+L2.dwholesale_p+L3.dwholesale_p+L4.dwholesale_p+ ///
		   L5.dwholesale_p+L6.dwholesale_p+L7.dwholesale_p+L8.dwholesale_p+L9.dwholesale_p
		replace pt2p=r(estimate) in 10
		replace pt2p_95u=r(estimate)+1.96*r(se) in 10
		replace pt2p_95l=r(estimate)-1.96*r(se) in 10			   
	lincom dwholesale_p+L1.dwholesale_p+L2.dwholesale_p+L3.dwholesale_p+L4.dwholesale_p+ ///
		   L5.dwholesale_p+L6.dwholesale_p+L7.dwholesale_p+L8.dwholesale_p+L9.dwholesale_p+ ///
		   L10.dwholesale_p
		replace pt2p=r(estimate) in 11
		replace pt2p_95u=r(estimate)+1.96*r(se) in 11
		replace pt2p_95l=r(estimate)-1.96*r(se) in 11			   
	lincom dwholesale_p+L1.dwholesale_p+L2.dwholesale_p+L3.dwholesale_p+L4.dwholesale_p+ ///
		   L5.dwholesale_p+L6.dwholesale_p+L7.dwholesale_p+L8.dwholesale_p+L9.dwholesale_p+ ///
		   L10.dwholesale_p+L11.dwholesale_p
		replace pt2p=r(estimate) in 12
		replace pt2p_95u=r(estimate)+1.96*r(se) in 12
		replace pt2p_95l=r(estimate)-1.96*r(se) in 12	
	lincom dwholesale_p+L1.dwholesale_p+L2.dwholesale_p+L3.dwholesale_p+L4.dwholesale_p+ ///
		   L5.dwholesale_p+L6.dwholesale_p+L7.dwholesale_p+L8.dwholesale_p+L9.dwholesale_p+ ///
		   L10.dwholesale_p+L11.dwholesale_p+L12.dwholesale_p
		replace pt2p=r(estimate) in 13
		replace pt2p_95u=r(estimate)+1.96*r(se) in 13
		replace pt2p_95l=r(estimate)-1.96*r(se) in 13	
	lincom dwholesale_p+L1.dwholesale_p+L2.dwholesale_p+L3.dwholesale_p+L4.dwholesale_p+ ///
		   L5.dwholesale_p+L6.dwholesale_p+L7.dwholesale_p+L8.dwholesale_p+L9.dwholesale_p+ ///
		   L10.dwholesale_p+L11.dwholesale_p+L12.dwholesale_p+L13.dwholesale_p
		replace pt2p=r(estimate) in 14
		replace pt2p_95u=r(estimate)+1.96*r(se) in 14
		replace pt2p_95l=r(estimate)-1.96*r(se) in 14	
	lincom dwholesale_p+L1.dwholesale_p+L2.dwholesale_p+L3.dwholesale_p+L4.dwholesale_p+ ///
		   L5.dwholesale_p+L6.dwholesale_p+L7.dwholesale_p+L8.dwholesale_p+L9.dwholesale_p+ ///
		   L10.dwholesale_p+L11.dwholesale_p+L12.dwholesale_p+L13.dwholesale_p+L14.dwholesale_p
		replace pt2p=r(estimate) in 15
		replace pt2p_95u=r(estimate)+1.96*r(se) in 15
		replace pt2p_95l=r(estimate)-1.96*r(se) in 15			
	lincom dwholesale_p+L1.dwholesale_p+L2.dwholesale_p+L3.dwholesale_p+L4.dwholesale_p+ ///
		   L5.dwholesale_p+L6.dwholesale_p+L7.dwholesale_p+L8.dwholesale_p+L9.dwholesale_p+ ///
		   L10.dwholesale_p+L11.dwholesale_p+L12.dwholesale_p+L13.dwholesale_p+L14.dwholesale_p+ ///
		   L15.dwholesale_p
		replace pt2p=r(estimate) in 16
		replace pt2p_95u=r(estimate)+1.96*r(se) in 16
		replace pt2p_95l=r(estimate)-1.96*r(se) in 16					
	lincom dwholesale_p+L1.dwholesale_p+L2.dwholesale_p+L3.dwholesale_p+L4.dwholesale_p+ ///
		   L5.dwholesale_p+L6.dwholesale_p+L7.dwholesale_p+L8.dwholesale_p+L9.dwholesale_p+ ///
		   L10.dwholesale_p+L11.dwholesale_p+L12.dwholesale_p+L13.dwholesale_p+L14.dwholesale_p+ ///
		   L15.dwholesale_p+L16.dwholesale_p
		replace pt2p=r(estimate) in 17
		replace pt2p_95u=r(estimate)+1.96*r(se) in 17
		replace pt2p_95l=r(estimate)-1.96*r(se) in 17			
	lincom dwholesale_p+L1.dwholesale_p+L2.dwholesale_p+L3.dwholesale_p+L4.dwholesale_p+ ///
		   L5.dwholesale_p+L6.dwholesale_p+L7.dwholesale_p+L8.dwholesale_p+L9.dwholesale_p+ ///
		   L10.dwholesale_p+L11.dwholesale_p+L12.dwholesale_p+L13.dwholesale_p+L14.dwholesale_p+ ///
		   L15.dwholesale_p+L16.dwholesale_p+L17.dwholesale_p
		replace pt2p=r(estimate) in 18
		replace pt2p_95u=r(estimate)+1.96*r(se) in 18
		replace pt2p_95l=r(estimate)-1.96*r(se) in 18		
	lincom dwholesale_p+L1.dwholesale_p+L2.dwholesale_p+L3.dwholesale_p+L4.dwholesale_p+ ///
		   L5.dwholesale_p+L6.dwholesale_p+L7.dwholesale_p+L8.dwholesale_p+L9.dwholesale_p+ ///
		   L10.dwholesale_p+L11.dwholesale_p+L12.dwholesale_p+L13.dwholesale_p+L14.dwholesale_p+ ///
		   L15.dwholesale_p+L16.dwholesale_p+L17.dwholesale_p+L18.dwholesale_p
		replace pt2p=r(estimate) in 19
		replace pt2p_95u=r(estimate)+1.96*r(se) in 19
		replace pt2p_95l=r(estimate)-1.96*r(se) in 19		
	lincom dwholesale_p+L1.dwholesale_p+L2.dwholesale_p+L3.dwholesale_p+L4.dwholesale_p+ ///
		   L5.dwholesale_p+L6.dwholesale_p+L7.dwholesale_p+L8.dwholesale_p+L9.dwholesale_p+ ///
		   L10.dwholesale_p+L11.dwholesale_p+L12.dwholesale_p+L13.dwholesale_p+L14.dwholesale_p+ ///
		   L15.dwholesale_p+L16.dwholesale_p+L17.dwholesale_p+L18.dwholesale_p+L19.dwholesale_p
		replace pt2p=r(estimate) in 20
		replace pt2p_95u=r(estimate)+1.96*r(se) in 20
		replace pt2p_95l=r(estimate)-1.96*r(se) in 20
	lincom dwholesale_p+L1.dwholesale_p+L2.dwholesale_p+L3.dwholesale_p+L4.dwholesale_p+ ///
		   L5.dwholesale_p+L6.dwholesale_p+L7.dwholesale_p+L8.dwholesale_p+L9.dwholesale_p+ ///
		   L10.dwholesale_p+L11.dwholesale_p+L12.dwholesale_p+L13.dwholesale_p+L14.dwholesale_p+ ///
		   L15.dwholesale_p+L16.dwholesale_p+L17.dwholesale_p+L18.dwholesale_p+L19.dwholesale_p+ ///
		   L20.dwholesale_p
		replace pt2p=r(estimate) in 21
		replace pt2p_95u=r(estimate)+1.96*r(se) in 21
		replace pt2p_95l=r(estimate)-1.96*r(se) in 21		
	lincom dwholesale_p+L1.dwholesale_p+L2.dwholesale_p+L3.dwholesale_p+L4.dwholesale_p+ ///
		   L5.dwholesale_p+L6.dwholesale_p+L7.dwholesale_p+L8.dwholesale_p+L9.dwholesale_p+ ///
		   L10.dwholesale_p+L11.dwholesale_p+L12.dwholesale_p+L13.dwholesale_p+L14.dwholesale_p+ ///
		   L15.dwholesale_p+L16.dwholesale_p+L17.dwholesale_p+L18.dwholesale_p+L19.dwholesale_p+ ///
		   L20.dwholesale_p+L21.dwholesale_p
		replace pt2p=r(estimate) in 22
		replace pt2p_95u=r(estimate)+1.96*r(se) in 22
		replace pt2p_95l=r(estimate)-1.96*r(se) in 22			
	lincom dwholesale_p+L1.dwholesale_p+L2.dwholesale_p+L3.dwholesale_p+L4.dwholesale_p+ ///
		   L5.dwholesale_p+L6.dwholesale_p+L7.dwholesale_p+L8.dwholesale_p+L9.dwholesale_p+ ///
		   L10.dwholesale_p+L11.dwholesale_p+L12.dwholesale_p+L13.dwholesale_p+L14.dwholesale_p+ ///
		   L15.dwholesale_p+L16.dwholesale_p+L17.dwholesale_p+L18.dwholesale_p+L19.dwholesale_p+ ///
		   L20.dwholesale_p+L21.dwholesale_p+L22.dwholesale_p
		replace pt2p=r(estimate) in 23
		replace pt2p_95u=r(estimate)+1.96*r(se) in 23
		replace pt2p_95l=r(estimate)-1.96*r(se) in 23	
	lincom dwholesale_p+L1.dwholesale_p+L2.dwholesale_p+L3.dwholesale_p+L4.dwholesale_p+ ///
		   L5.dwholesale_p+L6.dwholesale_p+L7.dwholesale_p+L8.dwholesale_p+L9.dwholesale_p+ ///
		   L10.dwholesale_p+L11.dwholesale_p+L12.dwholesale_p+L13.dwholesale_p+L14.dwholesale_p+ ///
		   L15.dwholesale_p+L16.dwholesale_p+L17.dwholesale_p+L18.dwholesale_p+L19.dwholesale_p+ ///
		   L20.dwholesale_p+L21.dwholesale_p+L22.dwholesale_p+L23.dwholesale_p
		replace pt2p=r(estimate) in 24
		replace pt2p_95u=r(estimate)+1.96*r(se) in 24
		replace pt2p_95l=r(estimate)-1.96*r(se) in 24	
	lincom dwholesale_p+L1.dwholesale_p+L2.dwholesale_p+L3.dwholesale_p+L4.dwholesale_p+ ///
		   L5.dwholesale_p+L6.dwholesale_p+L7.dwholesale_p+L8.dwholesale_p+L9.dwholesale_p+ ///
		   L10.dwholesale_p+L11.dwholesale_p+L12.dwholesale_p+L13.dwholesale_p+L14.dwholesale_p+ ///
		   L15.dwholesale_p+L16.dwholesale_p+L17.dwholesale_p+L18.dwholesale_p+L19.dwholesale_p+ ///
		   L20.dwholesale_p+L21.dwholesale_p+L22.dwholesale_p+L23.dwholesale_p+L24.dwholesale_p
		replace pt2p=r(estimate) in 25
		replace pt2p_95u=r(estimate)+1.96*r(se) in 25
		replace pt2p_95l=r(estimate)-1.96*r(se) in 25
	lincom dwholesale_p+L1.dwholesale_p+L2.dwholesale_p+L3.dwholesale_p+L4.dwholesale_p+ ///
		   L5.dwholesale_p+L6.dwholesale_p+L7.dwholesale_p+L8.dwholesale_p+L9.dwholesale_p+ ///
		   L10.dwholesale_p+L11.dwholesale_p+L12.dwholesale_p+L13.dwholesale_p+L14.dwholesale_p+ ///
		   L15.dwholesale_p+L16.dwholesale_p+L17.dwholesale_p+L18.dwholesale_p+L19.dwholesale_p+ ///
		   L20.dwholesale_p+L21.dwholesale_p+L22.dwholesale_p+L23.dwholesale_p+L24.dwholesale_p+ ///
		   L25.dwholesale_p
		replace pt2p=r(estimate) in 26
		replace pt2p_95u=r(estimate)+1.96*r(se) in 26
		replace pt2p_95l=r(estimate)-1.96*r(se) in 26		
	lincom dwholesale_p+L1.dwholesale_p+L2.dwholesale_p+L3.dwholesale_p+L4.dwholesale_p+ ///
		   L5.dwholesale_p+L6.dwholesale_p+L7.dwholesale_p+L8.dwholesale_p+L9.dwholesale_p+ ///
		   L10.dwholesale_p+L11.dwholesale_p+L12.dwholesale_p+L13.dwholesale_p+L14.dwholesale_p+ ///
		   L15.dwholesale_p+L16.dwholesale_p+L17.dwholesale_p+L18.dwholesale_p+L19.dwholesale_p+ ///
		   L20.dwholesale_p+L21.dwholesale_p+L22.dwholesale_p+L23.dwholesale_p+L24.dwholesale_p+ ///
		   L25.dwholesale_p+L26.dwholesale_p
		replace pt2p=r(estimate) in 27
		replace pt2p_95u=r(estimate)+1.96*r(se) in 27
		replace pt2p_95l=r(estimate)-1.96*r(se) in 27	
	lincom dwholesale_p+L1.dwholesale_p+L2.dwholesale_p+L3.dwholesale_p+L4.dwholesale_p+ ///
		   L5.dwholesale_p+L6.dwholesale_p+L7.dwholesale_p+L8.dwholesale_p+L9.dwholesale_p+ ///
		   L10.dwholesale_p+L11.dwholesale_p+L12.dwholesale_p+L13.dwholesale_p+L14.dwholesale_p+ ///
		   L15.dwholesale_p+L16.dwholesale_p+L17.dwholesale_p+L18.dwholesale_p+L19.dwholesale_p+ ///
		   L20.dwholesale_p+L21.dwholesale_p+L22.dwholesale_p+L23.dwholesale_p+L24.dwholesale_p+ ///
		   L25.dwholesale_p+L26.dwholesale_p+L27.dwholesale_p
		replace pt2p=r(estimate) in 28
		replace pt2p_95u=r(estimate)+1.96*r(se) in 28
		replace pt2p_95l=r(estimate)-1.96*r(se) in 28			
	lincom dwholesale_p+L1.dwholesale_p+L2.dwholesale_p+L3.dwholesale_p+L4.dwholesale_p+ ///
		   L5.dwholesale_p+L6.dwholesale_p+L7.dwholesale_p+L8.dwholesale_p+L9.dwholesale_p+ ///
		   L10.dwholesale_p+L11.dwholesale_p+L12.dwholesale_p+L13.dwholesale_p+L14.dwholesale_p+ ///
		   L15.dwholesale_p+L16.dwholesale_p+L17.dwholesale_p+L18.dwholesale_p+L19.dwholesale_p+ ///
		   L20.dwholesale_p+L21.dwholesale_p+L22.dwholesale_p+L23.dwholesale_p+L24.dwholesale_p+ ///
		   L25.dwholesale_p+L26.dwholesale_p+L27.dwholesale_p+L28.dwholesale_p
		replace pt2p=r(estimate) in 29
		replace pt2p_95u=r(estimate)+1.96*r(se) in 29
		replace pt2p_95l=r(estimate)-1.96*r(se) in 29
	lincom dwholesale_p+L1.dwholesale_p+L2.dwholesale_p+L3.dwholesale_p+L4.dwholesale_p+ ///
		   L5.dwholesale_p+L6.dwholesale_p+L7.dwholesale_p+L8.dwholesale_p+L9.dwholesale_p+ ///
		   L10.dwholesale_p+L11.dwholesale_p+L12.dwholesale_p+L13.dwholesale_p+L14.dwholesale_p+ ///
		   L15.dwholesale_p+L16.dwholesale_p+L17.dwholesale_p+L18.dwholesale_p+L19.dwholesale_p+ ///
		   L20.dwholesale_p+L21.dwholesale_p+L22.dwholesale_p+L23.dwholesale_p+L24.dwholesale_p+ ///
		   L25.dwholesale_p+L26.dwholesale_p+L27.dwholesale_p+L28.dwholesale_p+L29.dwholesale_p
		replace pt2p=r(estimate) in 30
		replace pt2p_95u=r(estimate)+1.96*r(se) in 30
		replace pt2p_95l=r(estimate)-1.96*r(se) in 30
	lincom dwholesale_p+L1.dwholesale_p+L2.dwholesale_p+L3.dwholesale_p+L4.dwholesale_p+ ///
		   L5.dwholesale_p+L6.dwholesale_p+L7.dwholesale_p+L8.dwholesale_p+L9.dwholesale_p+ ///
		   L10.dwholesale_p+L11.dwholesale_p+L12.dwholesale_p+L13.dwholesale_p+L14.dwholesale_p+ ///
		   L15.dwholesale_p+L16.dwholesale_p+L17.dwholesale_p+L18.dwholesale_p+L19.dwholesale_p+ ///
		   L20.dwholesale_p+L21.dwholesale_p+L22.dwholesale_p+L23.dwholesale_p+L24.dwholesale_p+ ///
		   L25.dwholesale_p+L26.dwholesale_p+L27.dwholesale_p+L28.dwholesale_p+L29.dwholesale_p+ ///
		   L30.dwholesale_p
		replace pt2p=r(estimate) in 31
		replace pt2p_95u=r(estimate)+1.96*r(se) in 31
		replace pt2p_95l=r(estimate)-1.96*r(se) in 31	
		
*Negative Shocks
	replace pt2n=_b[dwholesale_n] in 1
		replace pt2n_95l=_b[dwholesale_n]- 1.96*_se[dwholesale_n] in 1
		replace pt2n_95u=_b[dwholesale_n]+ 1.96*_se[dwholesale_n] in 1
	lincom dwholesale_n+L1.dwholesale_n
		replace pt2n=r(estimate) in 2
		replace pt2n_95u=r(estimate)+1.96*r(se) in 2
		replace pt2n_95l=r(estimate)-1.96*r(se) in 2
	lincom dwholesale_n+L1.dwholesale_n+L2.dwholesale_n
		replace pt2n=r(estimate) in 3
		replace pt2n_95u=r(estimate)+1.96*r(se) in 3
		replace pt2n_95l=r(estimate)-1.96*r(se) in 3
	lincom dwholesale_n+L1.dwholesale_n+L2.dwholesale_n+L3.dwholesale_n
		replace pt2n=r(estimate) in 4
		replace pt2n_95u=r(estimate)+1.96*r(se) in 4
		replace pt2n_95l=r(estimate)-1.96*r(se) in 4
	lincom dwholesale_n+L1.dwholesale_n+L2.dwholesale_n+L3.dwholesale_n+L4.dwholesale_n
		replace pt2n=r(estimate) in 5
		replace pt2n_95u=r(estimate)+1.96*r(se) in 5
		replace pt2n_95l=r(estimate)-1.96*r(se) in 5
	lincom dwholesale_n+L1.dwholesale_n+L2.dwholesale_n+L3.dwholesale_n+L4.dwholesale_n+ ///
		   L5.dwholesale_n
		replace pt2n=r(estimate) in 6
		replace pt2n_95u=r(estimate)+1.96*r(se) in 6
		replace pt2n_95l=r(estimate)-1.96*r(se) in 6			
	lincom dwholesale_n+L1.dwholesale_n+L2.dwholesale_n+L3.dwholesale_n+L4.dwholesale_n+ ///
		   L5.dwholesale_n+L6.dwholesale_n
		replace pt2n=r(estimate) in 7
		replace pt2n_95u=r(estimate)+1.96*r(se) in 7
		replace pt2n_95l=r(estimate)-1.96*r(se) in 7	
	lincom dwholesale_n+L1.dwholesale_n+L2.dwholesale_n+L3.dwholesale_n+L4.dwholesale_n+ ///
		   L5.dwholesale_n+L6.dwholesale_n+L7.dwholesale_n
		replace pt2n=r(estimate) in 8
		replace pt2n_95u=r(estimate)+1.96*r(se) in 8
		replace pt2n_95l=r(estimate)-1.96*r(se) in 8			
	lincom dwholesale_n+L1.dwholesale_n+L2.dwholesale_n+L3.dwholesale_n+L4.dwholesale_n+ ///
		   L5.dwholesale_n+L6.dwholesale_n+L7.dwholesale_n+L8.dwholesale_n
		replace pt2n=r(estimate) in 9
		replace pt2n_95u=r(estimate)+1.96*r(se) in 9
		replace pt2n_95l=r(estimate)-1.96*r(se) in 9			
	lincom dwholesale_n+L1.dwholesale_n+L2.dwholesale_n+L3.dwholesale_n+L4.dwholesale_n+ ///
		   L5.dwholesale_n+L6.dwholesale_n+L7.dwholesale_n+L8.dwholesale_n+L9.dwholesale_n
		replace pt2n=r(estimate) in 10
		replace pt2n_95u=r(estimate)+1.96*r(se) in 10
		replace pt2n_95l=r(estimate)-1.96*r(se) in 10			   
	lincom dwholesale_n+L1.dwholesale_n+L2.dwholesale_n+L3.dwholesale_n+L4.dwholesale_n+ ///
		   L5.dwholesale_n+L6.dwholesale_n+L7.dwholesale_n+L8.dwholesale_n+L9.dwholesale_n+ ///
		   L10.dwholesale_n
		replace pt2n=r(estimate) in 11
		replace pt2n_95u=r(estimate)+1.96*r(se) in 11
		replace pt2n_95l=r(estimate)-1.96*r(se) in 11			   
	lincom dwholesale_n+L1.dwholesale_n+L2.dwholesale_n+L3.dwholesale_n+L4.dwholesale_n+ ///
		   L5.dwholesale_n+L6.dwholesale_n+L7.dwholesale_n+L8.dwholesale_n+L9.dwholesale_n+ ///
		   L10.dwholesale_n+L11.dwholesale_n
		replace pt2n=r(estimate) in 12
		replace pt2n_95u=r(estimate)+1.96*r(se) in 12
		replace pt2n_95l=r(estimate)-1.96*r(se) in 12	
	lincom dwholesale_n+L1.dwholesale_n+L2.dwholesale_n+L3.dwholesale_n+L4.dwholesale_n+ ///
		   L5.dwholesale_n+L6.dwholesale_n+L7.dwholesale_n+L8.dwholesale_n+L9.dwholesale_n+ ///
		   L10.dwholesale_n+L11.dwholesale_n+L12.dwholesale_n
		replace pt2n=r(estimate) in 13
		replace pt2n_95u=r(estimate)+1.96*r(se) in 13
		replace pt2n_95l=r(estimate)-1.96*r(se) in 13	
	lincom dwholesale_n+L1.dwholesale_n+L2.dwholesale_n+L3.dwholesale_n+L4.dwholesale_n+ ///
		   L5.dwholesale_n+L6.dwholesale_n+L7.dwholesale_n+L8.dwholesale_n+L9.dwholesale_n+ ///
		   L10.dwholesale_n+L11.dwholesale_n+L12.dwholesale_n+L13.dwholesale_n
		replace pt2n=r(estimate) in 14
		replace pt2n_95u=r(estimate)+1.96*r(se) in 14
		replace pt2n_95l=r(estimate)-1.96*r(se) in 14	
	lincom dwholesale_n+L1.dwholesale_n+L2.dwholesale_n+L3.dwholesale_n+L4.dwholesale_n+ ///
		   L5.dwholesale_n+L6.dwholesale_n+L7.dwholesale_n+L8.dwholesale_n+L9.dwholesale_n+ ///
		   L10.dwholesale_n+L11.dwholesale_n+L12.dwholesale_n+L13.dwholesale_n+L14.dwholesale_n
		replace pt2n=r(estimate) in 15
		replace pt2n_95u=r(estimate)+1.96*r(se) in 15
		replace pt2n_95l=r(estimate)-1.96*r(se) in 15			
	lincom dwholesale_n+L1.dwholesale_n+L2.dwholesale_n+L3.dwholesale_n+L4.dwholesale_n+ ///
		   L5.dwholesale_n+L6.dwholesale_n+L7.dwholesale_n+L8.dwholesale_n+L9.dwholesale_n+ ///
		   L10.dwholesale_n+L11.dwholesale_n+L12.dwholesale_n+L13.dwholesale_n+L14.dwholesale_n+ ///
		   L15.dwholesale_n
		replace pt2n=r(estimate) in 16
		replace pt2n_95u=r(estimate)+1.96*r(se) in 16
		replace pt2n_95l=r(estimate)-1.96*r(se) in 16					
	lincom dwholesale_n+L1.dwholesale_n+L2.dwholesale_n+L3.dwholesale_n+L4.dwholesale_n+ ///
		   L5.dwholesale_n+L6.dwholesale_n+L7.dwholesale_n+L8.dwholesale_n+L9.dwholesale_n+ ///
		   L10.dwholesale_n+L11.dwholesale_n+L12.dwholesale_n+L13.dwholesale_n+L14.dwholesale_n+ ///
		   L15.dwholesale_n+L16.dwholesale_n
		replace pt2n=r(estimate) in 17
		replace pt2n_95u=r(estimate)+1.96*r(se) in 17
		replace pt2n_95l=r(estimate)-1.96*r(se) in 17			
	lincom dwholesale_n+L1.dwholesale_n+L2.dwholesale_n+L3.dwholesale_n+L4.dwholesale_n+ ///
		   L5.dwholesale_n+L6.dwholesale_n+L7.dwholesale_n+L8.dwholesale_n+L9.dwholesale_n+ ///
		   L10.dwholesale_n+L11.dwholesale_n+L12.dwholesale_n+L13.dwholesale_n+L14.dwholesale_n+ ///
		   L15.dwholesale_n+L16.dwholesale_n+L17.dwholesale_n
		replace pt2n=r(estimate) in 18
		replace pt2n_95u=r(estimate)+1.96*r(se) in 18
		replace pt2n_95l=r(estimate)-1.96*r(se) in 18		
	lincom dwholesale_n+L1.dwholesale_n+L2.dwholesale_n+L3.dwholesale_n+L4.dwholesale_n+ ///
		   L5.dwholesale_n+L6.dwholesale_n+L7.dwholesale_n+L8.dwholesale_n+L9.dwholesale_n+ ///
		   L10.dwholesale_n+L11.dwholesale_n+L12.dwholesale_n+L13.dwholesale_n+L14.dwholesale_n+ ///
		   L15.dwholesale_n+L16.dwholesale_n+L17.dwholesale_n+L18.dwholesale_n
		replace pt2n=r(estimate) in 19
		replace pt2n_95u=r(estimate)+1.96*r(se) in 19
		replace pt2n_95l=r(estimate)-1.96*r(se) in 19		
	lincom dwholesale_n+L1.dwholesale_n+L2.dwholesale_n+L3.dwholesale_n+L4.dwholesale_n+ ///
		   L5.dwholesale_n+L6.dwholesale_n+L7.dwholesale_n+L8.dwholesale_n+L9.dwholesale_n+ ///
		   L10.dwholesale_n+L11.dwholesale_n+L12.dwholesale_n+L13.dwholesale_n+L14.dwholesale_n+ ///
		   L15.dwholesale_n+L16.dwholesale_n+L17.dwholesale_n+L18.dwholesale_n+L19.dwholesale_n
		replace pt2n=r(estimate) in 20
		replace pt2n_95u=r(estimate)+1.96*r(se) in 20
		replace pt2n_95l=r(estimate)-1.96*r(se) in 20
	lincom dwholesale_n+L1.dwholesale_n+L2.dwholesale_n+L3.dwholesale_n+L4.dwholesale_n+ ///
		   L5.dwholesale_n+L6.dwholesale_n+L7.dwholesale_n+L8.dwholesale_n+L9.dwholesale_n+ ///
		   L10.dwholesale_n+L11.dwholesale_n+L12.dwholesale_n+L13.dwholesale_n+L14.dwholesale_n+ ///
		   L15.dwholesale_n+L16.dwholesale_n+L17.dwholesale_n+L18.dwholesale_n+L19.dwholesale_n+ ///
		   L20.dwholesale_n
		replace pt2n=r(estimate) in 21
		replace pt2n_95u=r(estimate)+1.96*r(se) in 21
		replace pt2n_95l=r(estimate)-1.96*r(se) in 21		
	lincom dwholesale_n+L1.dwholesale_n+L2.dwholesale_n+L3.dwholesale_n+L4.dwholesale_n+ ///
		   L5.dwholesale_n+L6.dwholesale_n+L7.dwholesale_n+L8.dwholesale_n+L9.dwholesale_n+ ///
		   L10.dwholesale_n+L11.dwholesale_n+L12.dwholesale_n+L13.dwholesale_n+L14.dwholesale_n+ ///
		   L15.dwholesale_n+L16.dwholesale_n+L17.dwholesale_n+L18.dwholesale_n+L19.dwholesale_n+ ///
		   L20.dwholesale_n+L21.dwholesale_n
		replace pt2n=r(estimate) in 22
		replace pt2n_95u=r(estimate)+1.96*r(se) in 22
		replace pt2n_95l=r(estimate)-1.96*r(se) in 22			
	lincom dwholesale_n+L1.dwholesale_n+L2.dwholesale_n+L3.dwholesale_n+L4.dwholesale_n+ ///
		   L5.dwholesale_n+L6.dwholesale_n+L7.dwholesale_n+L8.dwholesale_n+L9.dwholesale_n+ ///
		   L10.dwholesale_n+L11.dwholesale_n+L12.dwholesale_n+L13.dwholesale_n+L14.dwholesale_n+ ///
		   L15.dwholesale_n+L16.dwholesale_n+L17.dwholesale_n+L18.dwholesale_n+L19.dwholesale_n+ ///
		   L20.dwholesale_n+L21.dwholesale_n+L22.dwholesale_n
		replace pt2n=r(estimate) in 23
		replace pt2n_95u=r(estimate)+1.96*r(se) in 23
		replace pt2n_95l=r(estimate)-1.96*r(se) in 23	
	lincom dwholesale_n+L1.dwholesale_n+L2.dwholesale_n+L3.dwholesale_n+L4.dwholesale_n+ ///
		   L5.dwholesale_n+L6.dwholesale_n+L7.dwholesale_n+L8.dwholesale_n+L9.dwholesale_n+ ///
		   L10.dwholesale_n+L11.dwholesale_n+L12.dwholesale_n+L13.dwholesale_n+L14.dwholesale_n+ ///
		   L15.dwholesale_n+L16.dwholesale_n+L17.dwholesale_n+L18.dwholesale_n+L19.dwholesale_n+ ///
		   L20.dwholesale_n+L21.dwholesale_n+L22.dwholesale_n+L23.dwholesale_n
		replace pt2n=r(estimate) in 24
		replace pt2n_95u=r(estimate)+1.96*r(se) in 24
		replace pt2n_95l=r(estimate)-1.96*r(se) in 24	
	lincom dwholesale_n+L1.dwholesale_n+L2.dwholesale_n+L3.dwholesale_n+L4.dwholesale_n+ ///
		   L5.dwholesale_n+L6.dwholesale_n+L7.dwholesale_n+L8.dwholesale_n+L9.dwholesale_n+ ///
		   L10.dwholesale_n+L11.dwholesale_n+L12.dwholesale_n+L13.dwholesale_n+L14.dwholesale_n+ ///
		   L15.dwholesale_n+L16.dwholesale_n+L17.dwholesale_n+L18.dwholesale_n+L19.dwholesale_n+ ///
		   L20.dwholesale_n+L21.dwholesale_n+L22.dwholesale_n+L23.dwholesale_n+L24.dwholesale_n
		replace pt2n=r(estimate) in 25
		replace pt2n_95u=r(estimate)+1.96*r(se) in 25
		replace pt2n_95l=r(estimate)-1.96*r(se) in 25
	lincom dwholesale_n+L1.dwholesale_n+L2.dwholesale_n+L3.dwholesale_n+L4.dwholesale_n+ ///
		   L5.dwholesale_n+L6.dwholesale_n+L7.dwholesale_n+L8.dwholesale_n+L9.dwholesale_n+ ///
		   L10.dwholesale_n+L11.dwholesale_n+L12.dwholesale_n+L13.dwholesale_n+L14.dwholesale_n+ ///
		   L15.dwholesale_n+L16.dwholesale_n+L17.dwholesale_n+L18.dwholesale_n+L19.dwholesale_n+ ///
		   L20.dwholesale_n+L21.dwholesale_n+L22.dwholesale_n+L23.dwholesale_n+L24.dwholesale_n+ ///
		   L25.dwholesale_n
		replace pt2n=r(estimate) in 26
		replace pt2n_95u=r(estimate)+1.96*r(se) in 26
		replace pt2n_95l=r(estimate)-1.96*r(se) in 26		
	lincom dwholesale_n+L1.dwholesale_n+L2.dwholesale_n+L3.dwholesale_n+L4.dwholesale_n+ ///
		   L5.dwholesale_n+L6.dwholesale_n+L7.dwholesale_n+L8.dwholesale_n+L9.dwholesale_n+ ///
		   L10.dwholesale_n+L11.dwholesale_n+L12.dwholesale_n+L13.dwholesale_n+L14.dwholesale_n+ ///
		   L15.dwholesale_n+L16.dwholesale_n+L17.dwholesale_n+L18.dwholesale_n+L19.dwholesale_n+ ///
		   L20.dwholesale_n+L21.dwholesale_n+L22.dwholesale_n+L23.dwholesale_n+L24.dwholesale_n+ ///
		   L25.dwholesale_n+L26.dwholesale_n
		replace pt2n=r(estimate) in 27
		replace pt2n_95u=r(estimate)+1.96*r(se) in 27
		replace pt2n_95l=r(estimate)-1.96*r(se) in 27	
	lincom dwholesale_n+L1.dwholesale_n+L2.dwholesale_n+L3.dwholesale_n+L4.dwholesale_n+ ///
		   L5.dwholesale_n+L6.dwholesale_n+L7.dwholesale_n+L8.dwholesale_n+L9.dwholesale_n+ ///
		   L10.dwholesale_n+L11.dwholesale_n+L12.dwholesale_n+L13.dwholesale_n+L14.dwholesale_n+ ///
		   L15.dwholesale_n+L16.dwholesale_n+L17.dwholesale_n+L18.dwholesale_n+L19.dwholesale_n+ ///
		   L20.dwholesale_n+L21.dwholesale_n+L22.dwholesale_n+L23.dwholesale_n+L24.dwholesale_n+ ///
		   L25.dwholesale_n+L26.dwholesale_n+L27.dwholesale_n
		replace pt2n=r(estimate) in 28
		replace pt2n_95u=r(estimate)+1.96*r(se) in 28
		replace pt2n_95l=r(estimate)-1.96*r(se) in 28			
	lincom dwholesale_n+L1.dwholesale_n+L2.dwholesale_n+L3.dwholesale_n+L4.dwholesale_n+ ///
		   L5.dwholesale_n+L6.dwholesale_n+L7.dwholesale_n+L8.dwholesale_n+L9.dwholesale_n+ ///
		   L10.dwholesale_n+L11.dwholesale_n+L12.dwholesale_n+L13.dwholesale_n+L14.dwholesale_n+ ///
		   L15.dwholesale_n+L16.dwholesale_n+L17.dwholesale_n+L18.dwholesale_n+L19.dwholesale_n+ ///
		   L20.dwholesale_n+L21.dwholesale_n+L22.dwholesale_n+L23.dwholesale_n+L24.dwholesale_n+ ///
		   L25.dwholesale_n+L26.dwholesale_n+L27.dwholesale_n+L28.dwholesale_n
		replace pt2n=r(estimate) in 29
		replace pt2n_95u=r(estimate)+1.96*r(se) in 29
		replace pt2n_95l=r(estimate)-1.96*r(se) in 29
	lincom dwholesale_n+L1.dwholesale_n+L2.dwholesale_n+L3.dwholesale_n+L4.dwholesale_n+ ///
		   L5.dwholesale_n+L6.dwholesale_n+L7.dwholesale_n+L8.dwholesale_n+L9.dwholesale_n+ ///
		   L10.dwholesale_n+L11.dwholesale_n+L12.dwholesale_n+L13.dwholesale_n+L14.dwholesale_n+ ///
		   L15.dwholesale_n+L16.dwholesale_n+L17.dwholesale_n+L18.dwholesale_n+L19.dwholesale_n+ ///
		   L20.dwholesale_n+L21.dwholesale_n+L22.dwholesale_n+L23.dwholesale_n+L24.dwholesale_n+ ///
		   L25.dwholesale_n+L26.dwholesale_n+L27.dwholesale_n+L28.dwholesale_n+L29.dwholesale_n
		replace pt2n=r(estimate) in 30
		replace pt2n_95u=r(estimate)+1.96*r(se) in 30
		replace pt2n_95l=r(estimate)-1.96*r(se) in 30
	lincom dwholesale_n+L1.dwholesale_n+L2.dwholesale_n+L3.dwholesale_n+L4.dwholesale_n+ ///
		   L5.dwholesale_n+L6.dwholesale_n+L7.dwholesale_n+L8.dwholesale_n+L9.dwholesale_n+ ///
		   L10.dwholesale_n+L11.dwholesale_n+L12.dwholesale_n+L13.dwholesale_n+L14.dwholesale_n+ ///
		   L15.dwholesale_n+L16.dwholesale_n+L17.dwholesale_n+L18.dwholesale_n+L19.dwholesale_n+ ///
		   L20.dwholesale_n+L21.dwholesale_n+L22.dwholesale_n+L23.dwholesale_n+L24.dwholesale_n+ ///
		   L25.dwholesale_n+L26.dwholesale_n+L27.dwholesale_n+L28.dwholesale_n+L29.dwholesale_n+ ///
		   L30.dwholesale_n
		replace pt2n=r(estimate) in 31
		replace pt2n_95u=r(estimate)+1.96*r(se) in 31
		replace pt2n_95l=r(estimate)-1.96*r(se) in 31
				
		
*Pass-Through
twoway scatter pt2p event_d1, m(diamond) mc(edkblue) msize(medsmall) || /// 
	   rcap pt2p_95u pt2p_95l event_d1, lstyle(ci) lc(edkblue) || ///
	   rcap pt2n_95u pt2n_95l event_d2, lstyle(ci) lc(cranberry) || ///
	   scatter pt2n event_d2, m(triangle) mc(cranberry) msize(medsmall)  ///                 
	   graphregion(color(white)) bgcolor(white) ///
	   legend(order(1 "Positive" 4 "Negative" ) ///
	   rows(1) region(lcolor(white))) ///
	   xtitle("Days After $1/gallon Wholesale Cost Shock") ///  
	   ytitle(Pass Through ($/gal)) xlabel(0(3)30, nogrid)  ///
	   ylabel(0(0.5)1, nogrid)  ///
	   yline(0 0.5  1, lstyle(grid) lcolor(black*0.05))
graph export $figs/pt_landfall_asym_$outputdate.png, replace width(4000)
frame change default 				
			
	
********************************************************************************
*FIGURE B.11: PRICE EVENT STUDY SAMPLE SENSITIVITY
********************************************************************************
frame copy default event_sample, replace	

*Hurricane landfalls day 0
gsort station_id date
gen hur_landfall_d0=0 
	replace hur_landfall_d0=1 if BONCHAR_landfall==1 & date==`=td(12aug2004)'
	replace hur_landfall_d0=1 if FRANCES_landfall==1 & date==`=td(06sep2004)'
	replace hur_landfall_d0=1 if IVAN_landfall==1 & date==`=td(16sep2004)'
	replace hur_landfall_d0=1 if JEANNE_landfall==1 & date==`=td(26sep2004)'
	replace hur_landfall_d0=1 if ARLENE_landfall==1 & date==`=td(11jun2005)'
	replace hur_landfall_d0=1 if DENNIS_landfall==1 & date==`=td(10jul2005)'
	replace hur_landfall_d0=1 if KATRINA_FL_landfall==1 & date==`=td(25aug2005)'
	replace hur_landfall_d0=1 if KATRINA_LA_landfall==1 & date==`=td(29aug2005)'
	replace hur_landfall_d0=1 if RITA_landfall==1 & date==`=td(24sep2005)'
	replace hur_landfall_d0=1 if WILMA_landfall==1 & date==`=td(24oct2005)'
	replace hur_landfall_d0=1 if ALBERTO_landfall==1 & date==`=td(13jun2006)'
	replace hur_landfall_d0=1 if HUMBERTO_landfall==1 & date==`=td(13sep2007)'
	replace hur_landfall_d0=1 if GUSTAV_landfall==1 & date==`=td(01sep2008)'
	replace hur_landfall_d0=1 if IKE_landfall==1 & date==`=td(13sep2008)'
	
****
*Creating 14-day event windows   
gen event_day=. //Event day variable
	replace event_day=0 if hur_landfall_d0==1
	
*Indicators: 14 Days Prior to Hurricane for Different Samples
forvalues t = 1/14 {
	local n=-1*(`t')
	*Year check
	gen y=f`t'.year
	*Landfall 
	gen hur_landfall_dn`t' = 0
		replace hur_landfall_dn`t' = 1 if f`t'.hur_landfall_d0==1 & y==year
	*Event day  
	replace event_day=`n' if hur_landfall_dn`t'==1
		
	drop y
}
*
*Indicators: 14 Days After Hurricane
forvalues t = 1/14 {
	*Year check
	gen y=l`t'.year
	*Landfall indicator
	gen hur_landfall_d`t' = 0		
		replace hur_landfall_d`t' = 1 if l`t'.hur_landfall_d0==1 & y==year
	*Event day  
	replace event_day=`t' if hur_landfall_d`t'==1
	drop y		
}
*
order hur_landfall_dn14 hur_landfall_dn13 hur_landfall_dn12 hur_landfall_dn11 ///
      hur_landfall_dn10 hur_landfall_dn9 hur_landfall_dn8 hur_landfall_dn7 ///
	  hur_landfall_dn6 hur_landfall_dn5 hur_landfall_dn4 hur_landfall_dn3 ///
	  hur_landfall_dn2 hur_landfall_dn1 hur_landfall_d0 hur_landfall_d1 ///
	  hur_landfall_d2 hur_landfall_d3 hur_landfall_d4 hur_landfall_d5 ///
	  hur_landfall_d6 hur_landfall_d7 hur_landfall_d8 hur_landfall_d9 ///
	  hur_landfall_d10 hur_landfall_d11 hur_landfall_d12 hur_landfall_d13 ///
	  hur_landfall_d14, last
*
*Event window indicators 
egen window_landfall=rowtotal(hur_landfall_dn14-hur_landfall_d14)  //Event study - Landfall
*
gen d=. in 1
gen xb_hur1 = . in 1
	gen hi_hur1 = . in 1
	gen lo_hur1 = . in 1	
gen xb_hur2 = . in 1
	gen hi_hur2 = . in 1
	gen lo_hur2 = . in 1
gen xb_hur3 = . in 1
	gen hi_hur3 = . in 1
	gen lo_hur3 = . in 1
*
*****************
*PANEL (A): 75% REPORTING SAMPLE
reghdfe retail hur_landfall_dn14-hur_landfall_d14 ///
		CTST CHRT CTSN CHRN temp temp2 if window_landfall>=1 & sample_alt1==1, ///
	    absorb(station_id year month dow) cluster(county_FIPS)
*
replace d=-14 in 1
replace xb_hur1=0 in 1
replace hi_hur1=0 in 1
replace lo_hur1=0 in 1
*
forvalues i = 2/14 {
	local j=-1*(`i'-15)
	replace d=-1*`j' in `i'	
	lincom hur_landfall_dn`j'-hur_landfall_dn14
	replace xb_hur1    = r(estimate) in `i'
	replace hi_hur1 = r(estimate) + 1.96*r(se)  in `i'
	replace lo_hur1 = r(estimate) - 1.96*r(se) in `i'

}
*
forvalues i = 0/14 {
	local j=`i'+15
	replace d=`i' in `j'
	lincom hur_landfall_d`i'-hur_landfall_dn14
	replace xb_hur1    = r(estimate) in `j'
	replace hi_hur1 = r(estimate) + 1.96*r(se)  in `j'
	replace lo_hur1 = r(estimate) - 1.96*r(se) in `j'
}
*
*Graph
tw(connected xb_hur1 d, m(T) mfcolor(white) mlcolor(black) msize(medium) lcolor(edkblue) lwidth(medthick) mlwidth(medium)) ///
	(line hi_hur1 d, lpattern(dash) lcolor(erose)) ///
	(line lo_hur1 d, lpattern(dash) lcolor(erose)), ///
	graphr(color(white)) ///
	yscale(noline) ///
	xtit("Days Before/After Hurricane Landfall") ///
	ytit("Price ($/gal)") ///
	ylabel(-0.1(0.05)0.1 ,nogrid angle(0)) ///
	xlabel(-14(2)14) ///
	yline(0, lcolor(black))   ///
	xline(0,lcolor(black) lp(dash)) ///
	legend(off)  
graph export $figs/event_prices_alt1_$outputdate.png, replace width(4000)

*
*****************
*PANEL (A): 2004 75% REPORTING SAMPLE
reghdfe retail hur_landfall_dn14-hur_landfall_d14 ///
		CTST CHRT CTSN CHRN temp temp2 if window_landfall>=1 & sample_alt2==1, ///
	    absorb(station_id year month dow) cluster(county_FIPS)
*
replace d=-14 in 1
replace xb_hur1=0 in 1
replace hi_hur1=0 in 1
replace lo_hur1=0 in 1
*
forvalues i = 2/14 {
	local j=-1*(`i'-15)
	replace d=-1*`j' in `i'	
	lincom hur_landfall_dn`j'-hur_landfall_dn14
	replace xb_hur1    = r(estimate) in `i'
	replace hi_hur1 = r(estimate) + 1.96*r(se)  in `i'
	replace lo_hur1 = r(estimate) - 1.96*r(se) in `i'

}
*
forvalues i = 0/14 {
	local j=`i'+15
	replace d=`i' in `j'
	lincom hur_landfall_d`i'-hur_landfall_dn14
	replace xb_hur1    = r(estimate) in `j'
	replace hi_hur1 = r(estimate) + 1.96*r(se)  in `j'
	replace lo_hur1 = r(estimate) - 1.96*r(se) in `j'
}
*
*Graph
tw(connected xb_hur1 d, m(T) mfcolor(white) mlcolor(black) msize(medium) lcolor(edkblue) lwidth(medthick) mlwidth(medium)) ///
	(line hi_hur1 d, lpattern(dash) lcolor(erose)) ///
	(line lo_hur1 d, lpattern(dash) lcolor(erose)), ///
	graphr(color(white)) ///
	yscale(noline) ///
	xtit("Days Before/After Hurricane Landfall") ///
	ytit("Price ($/gal)") ///
	ylabel(-0.1(0.05)0.1 ,nogrid angle(0)) ///
	xlabel(-14(2)14) ///
	yline(0, lcolor(black))   ///
	xline(0,lcolor(black) lp(dash)) ///
	legend(off)  
graph export $figs/event_prices_alt2_$outputdate.png, replace width(4000)
*
*****************
*PANEL (C): ALL OPIS STATIONS  
reghdfe retail hur_landfall_dn14-hur_landfall_d14 ///
		CTST CHRT CTSN CHRN temp temp2 if window_landfall>=1, ///
	    absorb(station_id year month dow) cluster(county_FIPS)
*
replace d=-14 in 1
replace xb_hur1=0 in 1
replace hi_hur1=0 in 1
replace lo_hur1=0 in 1
*
forvalues i = 2/14 {
	local j=-1*(`i'-15)
	replace d=-1*`j' in `i'	
	lincom hur_landfall_dn`j'-hur_landfall_dn14
	replace xb_hur1    = r(estimate) in `i'
	replace hi_hur1 = r(estimate) + 1.96*r(se)  in `i'
	replace lo_hur1 = r(estimate) - 1.96*r(se) in `i'

}
*
forvalues i = 0/14 {
	local j=`i'+15
	replace d=`i' in `j'
	lincom hur_landfall_d`i'-hur_landfall_dn14
	replace xb_hur1    = r(estimate) in `j'
	replace hi_hur1 = r(estimate) + 1.96*r(se)  in `j'
	replace lo_hur1 = r(estimate) - 1.96*r(se) in `j'
}
*
*Graph
tw(connected xb_hur1 d, m(T) mfcolor(white) mlcolor(black) msize(medium) lcolor(edkblue) lwidth(medthick) mlwidth(medium)) ///
	(line hi_hur1 d, lpattern(dash) lcolor(erose)) ///
	(line lo_hur1 d, lpattern(dash) lcolor(erose)), ///
	graphr(color(white)) ///
	yscale(noline) ///
	xtit("Days Before/After Hurricane Landfall") ///
	ytit("Price ($/gal)") ///
	ylabel(-0.1(0.05)0.1 ,nogrid angle(0)) ///
	xlabel(-14(2)14) ///
	yline(0, lcolor(black))   ///
	xline(0,lcolor(black) lp(dash)) ///
	legend(off)  
graph export $figs/event_prices_alt3_$outputdate.png, replace width(4000)


********************************************************************************
*FIGURE B.12: MARGIN EVENT STUDY SAMPLE SENSITIVITY
********************************************************************************
*
*****************
*PANEL (A): 75% REPORTING SAMPLE
reghdfe retail hur_landfall_dn14-hur_landfall_d14 wholesale ///
		CTST CHRT CTSN CHRN temp temp2 if window_landfall>=1 & sample_alt1==1, ///
	    absorb(station_id year month dow) cluster(county_FIPS)
*
replace d=-14 in 1
replace xb_hur1=0 in 1
replace hi_hur1=0 in 1
replace lo_hur1=0 in 1
*
forvalues i = 2/14 {
	local j=-1*(`i'-15)
	replace d=-1*`j' in `i'	
	lincom hur_landfall_dn`j'-hur_landfall_dn14
	replace xb_hur1    = r(estimate) in `i'
	replace hi_hur1 = r(estimate) + 1.96*r(se)  in `i'
	replace lo_hur1 = r(estimate) - 1.96*r(se) in `i'

}
*
forvalues i = 0/14 {
	local j=`i'+15
	replace d=`i' in `j'
	lincom hur_landfall_d`i'-hur_landfall_dn14
	replace xb_hur1    = r(estimate) in `j'
	replace hi_hur1 = r(estimate) + 1.96*r(se)  in `j'
	replace lo_hur1 = r(estimate) - 1.96*r(se) in `j'
}
*
*Graph
tw(connected xb_hur1 d, m(O) mfcolor(white) mlcolor(black) msize(medium) lcolor(cranberry) lwidth(medthick) mlwidth(medium)) ///
	(line hi_hur1 d, lpattern(dash) lcolor(erose)) ///
	(line lo_hur1 d, lpattern(dash) lcolor(erose)), ///
	graphr(color(white)) ///
	yscale(noline) ///
	xtit("Days Before/After Hurricane Landfall") ///
	ytit("Price ($/gal)") ///
	ylabel(-0.1(0.05)0.1 ,nogrid angle(0)) ///
	xlabel(-14(2)14) ///
	yline(0, lcolor(black))   ///
	xline(0,lcolor(black) lp(dash)) ///
	legend(off)  
graph export $figs/event_margins_alt1_$outputdate.png, replace width(4000)
*
*****************
*PANEL (B): 2004 75% REPORTING SAMPLE 
reghdfe retail hur_landfall_dn14-hur_landfall_d14 wholesale ///
		CTST CHRT CTSN CHRN temp temp2 if window_landfall>=1 & sample_alt2==1, ///
	    absorb(station_id year month dow) cluster(county_FIPS)
*
replace d=-14 in 1
replace xb_hur1=0 in 1
replace hi_hur1=0 in 1
replace lo_hur1=0 in 1
*
forvalues i = 2/14 {
	local j=-1*(`i'-15)
	replace d=-1*`j' in `i'	
	lincom hur_landfall_dn`j'-hur_landfall_dn14
	replace xb_hur1    = r(estimate) in `i'
	replace hi_hur1 = r(estimate) + 1.96*r(se)  in `i'
	replace lo_hur1 = r(estimate) - 1.96*r(se) in `i'

}
*
forvalues i = 0/14 {
	local j=`i'+15
	replace d=`i' in `j'
	lincom hur_landfall_d`i'-hur_landfall_dn14
	replace xb_hur1    = r(estimate) in `j'
	replace hi_hur1 = r(estimate) + 1.96*r(se)  in `j'
	replace lo_hur1 = r(estimate) - 1.96*r(se) in `j'
}
*
*Graph
tw(connected xb_hur1 d, m(O) mfcolor(white) mlcolor(black) msize(medium) lcolor(cranberry) lwidth(medthick) mlwidth(medium)) ///
	(line hi_hur1 d, lpattern(dash) lcolor(erose)) ///
	(line lo_hur1 d, lpattern(dash) lcolor(erose)), ///
	graphr(color(white)) ///
	yscale(noline) ///
	xtit("Days Before/After Hurricane Landfall") ///
	ytit("Price ($/gal)") ///
	ylabel(-0.1(0.05)0.1 ,nogrid angle(0)) ///
	xlabel(-14(2)14) ///
	yline(0, lcolor(black))   ///
	xline(0,lcolor(black) lp(dash)) ///
	legend(off)  
graph export $figs/event_margins_alt2_$outputdate.png, replace width(4000)
*
*****************
*PANEL (C): ALL OPIS STATIONS
reghdfe retail hur_landfall_dn14-hur_landfall_d14 wholesale ///
		CTST CHRT CTSN CHRN temp temp2 if window_landfall>=1, ///
	    absorb(station_id year month dow) cluster(county_FIPS)
*
replace d=-14 in 1
replace xb_hur1=0 in 1
replace hi_hur1=0 in 1
replace lo_hur1=0 in 1
*
forvalues i = 2/14 {
	local j=-1*(`i'-15)
	replace d=-1*`j' in `i'	
	lincom hur_landfall_dn`j'-hur_landfall_dn14
	replace xb_hur1    = r(estimate) in `i'
	replace hi_hur1 = r(estimate) + 1.96*r(se)  in `i'
	replace lo_hur1 = r(estimate) - 1.96*r(se) in `i'

}
*
forvalues i = 0/14 {
	local j=`i'+15
	replace d=`i' in `j'
	lincom hur_landfall_d`i'-hur_landfall_dn14
	replace xb_hur1    = r(estimate) in `j'
	replace hi_hur1 = r(estimate) + 1.96*r(se)  in `j'
	replace lo_hur1 = r(estimate) - 1.96*r(se) in `j'
}
*
*Graph
tw(connected xb_hur1 d, m(O) mfcolor(white) mlcolor(black) msize(medium) lcolor(cranberry) lwidth(medthick) mlwidth(medium)) ///
	(line hi_hur1 d, lpattern(dash) lcolor(erose)) ///
	(line lo_hur1 d, lpattern(dash) lcolor(erose)), ///
	graphr(color(white)) ///
	yscale(noline) ///
	xtit("Days Before/After Hurricane Landfall") ///
	ytit("Price ($/gal)") ///
	ylabel(-0.1(0.05)0.1 ,nogrid angle(0)) ///
	xlabel(-14(2)14) ///
	yline(0, lcolor(black))   ///
	xline(0,lcolor(black) lp(dash)) ///
	legend(off)  
graph export $figs/event_margins_alt3_$outputdate.png, replace width(4000)
frame change default


********************************************************************************
*FIGURE B.13: MARGINS EVENT STUDY WITH DISTRIBUTED LAG WHOLESALE CONTROLS
********************************************************************************
frame copy default dl_event, replace
frame change dl_event
*
*Hurricane landfalls day 0
gsort station_id date
gen hur_landfall_d0=0 
	replace hur_landfall_d0=1 if BONCHAR_landfall==1 & date==`=td(12aug2004)'
	replace hur_landfall_d0=1 if FRANCES_landfall==1 & date==`=td(06sep2004)'
	replace hur_landfall_d0=1 if IVAN_landfall==1 & date==`=td(16sep2004)'
	replace hur_landfall_d0=1 if JEANNE_landfall==1 & date==`=td(26sep2004)'
	replace hur_landfall_d0=1 if ARLENE_landfall==1 & date==`=td(11jun2005)'
	replace hur_landfall_d0=1 if DENNIS_landfall==1 & date==`=td(10jul2005)'
	replace hur_landfall_d0=1 if KATRINA_FL_landfall==1 & date==`=td(25aug2005)'
	replace hur_landfall_d0=1 if KATRINA_LA_landfall==1 & date==`=td(29aug2005)'
	replace hur_landfall_d0=1 if RITA_landfall==1 & date==`=td(24sep2005)'
	replace hur_landfall_d0=1 if WILMA_landfall==1 & date==`=td(24oct2005)'
	replace hur_landfall_d0=1 if ALBERTO_landfall==1 & date==`=td(13jun2006)'
	replace hur_landfall_d0=1 if HUMBERTO_landfall==1 & date==`=td(13sep2007)'
	replace hur_landfall_d0=1 if GUSTAV_landfall==1 & date==`=td(01sep2008)'
	replace hur_landfall_d0=1 if IKE_landfall==1 & date==`=td(13sep2008)' 
gen event_day=. //Event day variable
	replace event_day=0 if hur_landfall_d0==1
*
*Indicators: 14 Days Prior to Hurricane for Different Samples
forvalues t = 1/14 {
	local n=-1*(`t')
	*Year check
	gen y=f`t'.year
	*Landfall 
	gen hur_landfall_dn`t' = 0
		replace hur_landfall_dn`t' = 1 if f`t'.hur_landfall_d0==1 & y==year
	*Event day  
	replace event_day=`n' if hur_landfall_dn`t'==1
	drop y
}
*
*Indicators: 14 Days After Hurricane
forvalues t = 1/14 {
	*Year check
	gen y=l`t'.year
	*Landfall indicator
	gen hur_landfall_d`t' = 0		
		replace hur_landfall_d`t' = 1 if l`t'.hur_landfall_d0==1 & y==year
	*Event day  
	replace event_day=`t' if hur_landfall_d`t'==1
	drop y		
}
*
order hur_landfall_dn14 hur_landfall_dn13 hur_landfall_dn12 hur_landfall_dn11 ///
      hur_landfall_dn10 hur_landfall_dn9 hur_landfall_dn8 hur_landfall_dn7 ///
	  hur_landfall_dn6 hur_landfall_dn5 hur_landfall_dn4 hur_landfall_dn3 ///
	  hur_landfall_dn2 hur_landfall_dn1 hur_landfall_d0 hur_landfall_d1 ///
	  hur_landfall_d2 hur_landfall_d3 hur_landfall_d4 hur_landfall_d5 ///
	  hur_landfall_d6 hur_landfall_d7 hur_landfall_d8 hur_landfall_d9 ///
	  hur_landfall_d10 hur_landfall_d11 hur_landfall_d12 hur_landfall_d13 ///
	  hur_landfall_d14, last
egen window_landfall=rowtotal(hur_landfall_dn14-hur_landfall_d14)  //Event study - Landfall
*
gen d=. in 1
gen xb_hur1 = . in 1
	gen hi_hur1 = . in 1
	gen lo_hur1 = . in 1	
gen xb_hur2 = . in 1
	gen hi_hur2 = . in 1
	gen lo_hur2 = . in 1
gen xb_hur3 = . in 1
	gen hi_hur3 = . in 1
	gen lo_hur3 = . in 1
*	
reghdfe retail hur_landfall_dn14-hur_landfall_d14 ///
		CTST CHRT CTSN CHRN temp temp2 ///
		l(0/29).d.wholesale l30.wholesale if window_landfall>=1, ///
	    absorb(station_id year month dow) cluster(county_FIPS)	
*			
replace d=-14 in 1
replace xb_hur1=0 in 1
replace hi_hur1=0 in 1
replace lo_hur1=0 in 1
*
forvalues i = 2/14 {
	local j=-1*(`i'-15)
	replace d=-1*`j' in `i'	
	lincom hur_landfall_dn`j'-hur_landfall_dn14
	replace xb_hur1    = r(estimate) in `i'
	replace hi_hur1 = r(estimate) + 1.96*r(se)  in `i'
	replace lo_hur1 = r(estimate) - 1.96*r(se) in `i'

}
*
forvalues i = 0/14 {
	local j=`i'+15
	replace d=`i' in `j'
	lincom hur_landfall_d`i'-hur_landfall_dn14
	replace xb_hur1    = r(estimate) in `j'
	replace hi_hur1 = r(estimate) + 1.96*r(se)  in `j'
	replace lo_hur1 = r(estimate) - 1.96*r(se) in `j'
}
*
*Graph
tw(connected xb_hur1 d, m(O) mfcolor(white) mlcolor(black) msize(medium) lcolor(cranberry) lwidth(medthick) mlwidth(medium)) ///
	(line hi_hur1 d, lpattern(dash) lcolor(erose)) ///
	(line lo_hur1 d, lpattern(dash) lcolor(erose)), ///
	graphr(color(white)) ///
	yscale(noline) ///
	xtit("Days Before/After Hurricane Landfall") ///
	ytit("Margin ($/gal)") ///
	ylabel(-0.1(0.05)0.1 ,nogrid angle(0)) ///
	xlabel(-14(2)14) ///
	yline(0, lcolor(black))   ///
	xline(0,lcolor(black) lp(dash)) ///
	legend(off)  
graph export $figs/event_margins_dlag_$outputdate.png, replace width(4000)
frame change default 


********************************************************************************
*FIGURE B.14: 21-DAY EVENT STUDIES
********************************************************************************
frame copy default event_ext, replace
frame change event_ext
keep if sample_main==1
*
*Hurricane landfalls day 0
gsort station_id date
gen hur_landfall_d0=0 
	replace hur_landfall_d0=1 if BONCHAR_landfall==1 & date==`=td(12aug2004)'
	replace hur_landfall_d0=1 if FRANCES_landfall==1 & date==`=td(06sep2004)'
	replace hur_landfall_d0=1 if IVAN_landfall==1 & date==`=td(16sep2004)'
	replace hur_landfall_d0=1 if JEANNE_landfall==1 & date==`=td(26sep2004)'
	replace hur_landfall_d0=1 if ARLENE_landfall==1 & date==`=td(11jun2005)'
	replace hur_landfall_d0=1 if DENNIS_landfall==1 & date==`=td(10jul2005)'
	replace hur_landfall_d0=1 if KATRINA_FL_landfall==1 & date==`=td(25aug2005)'
	replace hur_landfall_d0=1 if KATRINA_LA_landfall==1 & date==`=td(29aug2005)'
	replace hur_landfall_d0=1 if RITA_landfall==1 & date==`=td(24sep2005)'
	replace hur_landfall_d0=1 if WILMA_landfall==1 & date==`=td(24oct2005)'
	replace hur_landfall_d0=1 if ALBERTO_landfall==1 & date==`=td(13jun2006)'
	replace hur_landfall_d0=1 if HUMBERTO_landfall==1 & date==`=td(13sep2007)'
	replace hur_landfall_d0=1 if GUSTAV_landfall==1 & date==`=td(01sep2008)'
	replace hur_landfall_d0=1 if IKE_landfall==1 & date==`=td(13sep2008)'
*
gen event_day=. //Event day variable
	replace event_day=0 if hur_landfall_d0==1
*	
*Indicators: 21 Days Prior to Hurricane for Different Samples
forvalues t = 1/21 {
	local n=-1*(`t')
	*Year check
	gen y=f`t'.year
	*Landfall 
	gen hur_landfall_dn`t' = 0
		replace hur_landfall_dn`t' = 1 if f`t'.hur_landfall_d0==1 & y==year

	*Event day  
	replace event_day=`n' if hur_landfall_dn`t'==1
		
	drop y
}
*
*Indicators: 21 Days After Hurricane
forvalues t = 1/21 {
	*Year check
	gen y=l`t'.year
	*Landfall indicator
	gen hur_landfall_d`t' = 0		
		replace hur_landfall_d`t' = 1 if l`t'.hur_landfall_d0==1 & y==year
		
	*Event day  
	replace event_day=`t' if hur_landfall_d`t'==1
	drop y		
}
*
order hur_landfall_dn21 hur_landfall_dn20 hur_landfall_dn19 ///
	  hur_landfall_dn18 hur_landfall_dn17 hur_landfall_dn16 hur_landfall_dn15 ///
	  hur_landfall_dn14 hur_landfall_dn13 hur_landfall_dn12 hur_landfall_dn11 ///
      hur_landfall_dn10 hur_landfall_dn9 hur_landfall_dn8 hur_landfall_dn7 ///
	  hur_landfall_dn6 hur_landfall_dn5 hur_landfall_dn4 hur_landfall_dn3 ///
	  hur_landfall_dn2 hur_landfall_dn1 hur_landfall_d0 hur_landfall_d1 ///
	  hur_landfall_d2 hur_landfall_d3 hur_landfall_d4 hur_landfall_d5 ///
	  hur_landfall_d6 hur_landfall_d7 hur_landfall_d8 hur_landfall_d9 ///
	  hur_landfall_d10 hur_landfall_d11 hur_landfall_d12 hur_landfall_d13 ///
	  hur_landfall_d14 hur_landfall_d15 hur_landfall_d16 hur_landfall_d17 ///
	  hur_landfall_d18 hur_landfall_d19 hur_landfall_d20 hur_landfall_d21, last
egen window_landfall=rowtotal(hur_landfall_dn21-hur_landfall_d21)  //Event study - Landfall
*
gen d=. in 1
gen xb_hur1 = . in 1
	gen hi_hur1 = . in 1
	gen lo_hur1 = . in 1	
*	
*****************
*PANEL (A): PRICE IMPACTS
reghdfe retail hur_landfall_dn21-hur_landfall_d21 ///
		CTST CHRT CTSN CHRN temp temp2 if window_landfall>=1, ///
	    absorb(station_id year month dow) cluster(county_FIPS)
*
replace d=-21 in 1
replace xb_hur1=0 in 1
replace hi_hur1=0 in 1
replace lo_hur1=0 in 1
*
forvalues i = 2/21 {
	local j=-1*(`i'-22)
	replace d=-1*`j' in `i'	
	lincom hur_landfall_dn`j'-hur_landfall_dn21
	replace xb_hur1    = r(estimate) in `i'
	replace hi_hur1 = r(estimate) + 1.96*r(se)  in `i'
	replace lo_hur1 = r(estimate) - 1.96*r(se) in `i'

}
*
forvalues i = 0/21 {
	local j=`i'+22
	replace d=`i' in `j'
	lincom hur_landfall_d`i'-hur_landfall_dn21
	replace xb_hur1    = r(estimate) in `j'
	replace hi_hur1 = r(estimate) + 1.96*r(se)  in `j'
	replace lo_hur1 = r(estimate) - 1.96*r(se) in `j'
}
*
*Graph
tw(connected xb_hur1 d, m(T) mfcolor(white) mlcolor(black) msize(medium) lcolor(edkblue) lwidth(medthick) mlwidth(medium)) ///
	(line hi_hur1 d, lpattern(dash) lcolor(erose)) ///
	(line lo_hur1 d, lpattern(dash) lcolor(erose)), ///
	graphr(color(white)) ///
	yscale(noline) ///
	xtit("Days Before/After Hurricane Landfall") ///
	ytit("Price ($/gal)") ///
	ylabel(-0.1(0.05)0.1 ,nogrid angle(0)) ///
	yline(0, lcolor(black))   ///
	xline(0,lcolor(black) lp(dash)) ///
	legend(off)  
graph export $figs/event_prices_extended_$outputdate.png, replace width(4000)
drop d-lo_hur1
*	
*****************
*PANEL (B): MARGINS IMPACTS
gen d=. in 1
gen xb_hur1 = . in 1
	gen hi_hur1 = . in 1
	gen lo_hur1 = . in 1		
*
*Hurricane Landfall Areas - Margins		
reghdfe retail hur_landfall_dn21-hur_landfall_d21 ///
		CTST CHRT CTSN CHRN temp temp2 wholesale if window_landfall>=1, ///
	    absorb(station_id year month dow) cluster(county_FIPS)	
*
replace d=-21 in 1
replace xb_hur1=0 in 1
replace hi_hur1=0 in 1
replace lo_hur1=0 in 1
*
forvalues i = 2/21 {
	local j=-1*(`i'-22)
	replace d=-1*`j' in `i'	
	lincom hur_landfall_dn`j'-hur_landfall_dn21
	replace xb_hur1    = r(estimate) in `i'
	replace hi_hur1 = r(estimate) + 1.96*r(se)  in `i'
	replace lo_hur1 = r(estimate) - 1.96*r(se) in `i'

}
*
forvalues i = 0/21 {
	local j=`i'+22
	replace d=`i' in `j'
	lincom hur_landfall_d`i'-hur_landfall_dn21
	replace xb_hur1    = r(estimate) in `j'
	replace hi_hur1 = r(estimate) + 1.96*r(se)  in `j'
	replace lo_hur1 = r(estimate) - 1.96*r(se) in `j'
}
*
*Graph
tw(connected xb_hur1 d, m(O) mfcolor(white) mlcolor(black) msize(medium) lcolor(cranberry) lwidth(medthick) mlwidth(medium)) ///
	(line hi_hur1 d, lpattern(dash) lcolor(erose)) ///
	(line lo_hur1 d, lpattern(dash) lcolor(erose)), ///
	graphr(color(white)) ///
	yscale(noline) ///
	xtit("Days Before/After Hurricane Landfall") ///
	ytit("Margin ($/gal)") ///
	ylabel(-0.1(0.05)0.1 ,nogrid angle(0)) ///
	xlabel(-14(2)14) ///
	yline(0, lcolor(black))   ///
	xline(0,lcolor(black) lp(dash)) ///
	legend(off)  
graph export $figs/event_margins_extended_$outputdate.png, replace width(4000)
frame change default
	

********************************************************************************
*FIGURE B.15: EXTENDED WHOLESALE COST PASS-THROUGH
********************************************************************************
frame copy default pt_ext
frame change pt_ext
keep if sample_main==1
gsort station_id date
*
gen landfall_hur_sample=0 
	replace landfall_hur_sample=1 if BONCHAR_landfall==1 & date==`=td(12aug2004)'
	replace landfall_hur_sample=1 if FRANCES_landfall==1 & date==`=td(06sep2004)'
	replace landfall_hur_sample=1 if IVAN_landfall==1 & date==`=td(16sep2004)'
	replace landfall_hur_sample=1 if JEANNE_landfall==1 & date==`=td(26sep2004)'
	replace landfall_hur_sample=1 if ARLENE_landfall==1 & date==`=td(11jun2005)'
	replace landfall_hur_sample=1 if DENNIS_landfall==1 & date==`=td(10jul2005)'
	replace landfall_hur_sample=1 if KATRINA_FL_landfall==1 & date==`=td(25aug2005)'
	replace landfall_hur_sample=1 if KATRINA_LA_landfall==1 & date==`=td(29aug2005)'
	replace landfall_hur_sample=1 if RITA_landfall==1 & date==`=td(24sep2005)'
	replace landfall_hur_sample=1 if WILMA_landfall==1 & date==`=td(24oct2005)'
	replace landfall_hur_sample=1 if ALBERTO_landfall==1 & date==`=td(13jun2006)'
	replace landfall_hur_sample=1 if HUMBERTO_landfall==1 & date==`=td(13sep2007)'
	replace landfall_hur_sample=1 if GUSTAV_landfall==1 & date==`=td(01sep2008)'
	replace landfall_hur_sample=1 if IKE_landfall==1 & date==`=td(13sep2008)'
*
*Filling in pre-hurricane indicators
gen hur_temp=0 //First day of hurricane
	replace hur_temp=1 if landfall_hur_sample==1 & l.landfall_hur_sample==0
forvalues t = 1/45 {
	gen y=f`t'.year
	replace landfall_hur_sample=1 if f`t'.hur_temp==1 & y==year
	drop y
}
*
drop hur_temp
*
*Filling in post-hurricane indicators
gen hur_temp=0 //Last day of hurricane
	replace hur_temp=1 if landfall_hur_sample==1 & f.landfall_hur_sample==0	
forvalues t = 1/45 {
	gen y=l`t'.year
	replace landfall_hur_sample=1 if l`t'.hur_temp==1 & y==year
	drop y
}
*
drop hur_temp
*
*Landfall Sample
gen landfall_sample=0 
	replace landfall_sample=1 if BONCHAR_landfall==1 
	replace landfall_sample=1 if FRANCES_landfall==1  
	replace landfall_sample=1 if IVAN_landfall==1 
	replace landfall_sample=1 if JEANNE_landfall==1  
	replace landfall_sample=1 if ARLENE_landfall==1 
	replace landfall_sample=1 if DENNIS_landfall==1  
	replace landfall_sample=1 if KATRINA_FL_landfall==1 
	replace landfall_sample=1 if KATRINA_LA_landfall==1  
	replace landfall_sample=1 if RITA_landfall==1  
	replace landfall_sample=1 if WILMA_landfall==1 
	replace landfall_sample=1 if ALBERTO_landfall==1  
	replace landfall_sample=1 if HUMBERTO_landfall==1  
	replace landfall_sample=1 if GUSTAV_landfall==1  
	replace landfall_sample=1 if IKE_landfall==1  
*
*Variables
gen event_d=.
	label var event_d "Event Day"
gen pt1=.
	gen pt1_95l=.
	gen pt1_95u=.
	label var pt1 "All"
gen pt2=.
	gen pt2_95l=.
	gen pt2_95u=.
	label var pt2 "Landfall"
gen pt3=.
	gen pt3_95l=.
	gen pt3_95u=.
	label var pt3 "Landfall (Hurricane)"
*	
*Sample 1 - All Stations	
reghdfe retail l(0/44).d.wholesale l45.wholesale CTST CHRT CTSN CHRN temp temp2, ///
		absorb(station_id year month dow) cluster(county_FIPS)
*		   		   
*Day 0
replace event_d=0 in 1
replace pt1=_b[D1.wholesale]   in 1
replace pt1_95l=_b[D1.wholesale]-1.96*_se[D1.wholesale] in 1
replace pt1_95u=_b[D1.wholesale]+1.96*_se[D1.wholesale] in 1
*	
*Day 1
replace event_d=1 in 2
replace pt1=_b[LD.wholesale] in 2
replace pt1_95l=_b[LD.wholesale]-1.96*_se[LD.wholesale] in 2
replace pt1_95u=_b[LD.wholesale]+1.96*_se[LD.wholesale] in 2
*
forvalues i = 2/44 {
	local j=`i'+1
	replace event_d=`i' in `j'
	replace pt1=_b[L`i'D.wholesale] in `j'
	replace pt1_95l=_b[L`i'D.wholesale]-1.96*_se[L`i'D.wholesale] in `j'
	replace pt1_95u=_b[L`i'D.wholesale]+1.96*_se[L`i'D.wholesale] in `j'
}
*			   
*Day 45
replace event_d=45 in 46
replace pt1=_b[L45.wholesale] in 46
replace pt1_95l=_b[L45.wholesale]-1.96*_se[L45.wholesale] in 46
replace pt1_95u=_b[L45.wholesale]+1.96*_se[L45.wholesale] in 46
*
*Sample 2 - Landfall Stations (Hurricane Window)	
reghdfe retail l(0/44).d.wholesale l45.wholesale  CTST CHRT CTSN CHRN temp temp2 if landfall_hur_sample==1, ///
			   absorb(station_id year month dow) cluster(county_FIPS)
*	
*Day 0
replace pt3=_b[D1.wholesale]   in 1
replace pt3_95l=_b[D1.wholesale]-1.96*_se[D1.wholesale] in 1
replace pt3_95u=_b[D1.wholesale]+1.96*_se[D1.wholesale] in 1
*	
*Day 1
replace pt3=_b[LD.wholesale] in 2
replace pt3_95l=_b[LD.wholesale]-1.96*_se[L`i'D.wholesale] in 2
replace pt3_95u=_b[LD.wholesale]+1.96*_se[L`i'D.wholesale] in 2
*
forvalues i = 2/44 {
	local j=`i'+1
	replace pt3=_b[L`i'D.wholesale] in `j'
	replace pt3_95l=_b[L`i'D.wholesale]-1.96*_se[LD.wholesale] in `j'
	replace pt3_95u=_b[L`i'D.wholesale]+1.96*_se[LD.wholesale] in `j'
}
*			   
*Day 45
replace pt3=_b[L45.wholesale] in 46
replace pt3_95l=_b[L45.wholesale]-1.96*_se[L45.wholesale] in 46
replace pt3_95u=_b[L45.wholesale]+1.96*_se[L45.wholesale] in 46	
*
*Graphing 
keep event_d-pt3_95u
drop if event_d==.
gen event_d1=event_d+0.1
gen event_d2=event_d-0.1
twoway scatter pt1 event_d1, m(diamond) mc(edkblue) msize(medsmall) || /// 
	   rcap pt1_95u pt1_95l event_d1, lstyle(ci) lc(edkblue) || ///
	   rcap pt3_95u pt3_95l event_d2, lstyle(ci) lc(cranberry) || ///
	   scatter pt3 event_d2, m(triangle) mc(cranberry) msize(medsmall)  ///                 
	   graphregion(color(white)) bgcolor(white) ///
	   legend(order(1 "All Periods, Stations" 4 "Hurricane Window, Landfall Stations" ) ///
	   rows(1) region(lcolor(white))) ///
	   xtitle("Days After $1/gallon Wholesale Cost Shock") ///  
	   ytitle(Pass Through ($/gal)) xlabel(0(5)45, nogrid)  ///
	   ylabel(0(0.5)1, nogrid)  ///
	   yline(0 0.5  1, lstyle(grid) lcolor(black*0.05))
graph export $figs/pt_all_extend_$outputdate.png, replace width(4000)
frame change default	
	

********************************************************************************
*TABLE B.2: AVERAGE EFFECT OF HURRICANES ON RETAIL AND WHOLESALE PRICES AND MARGINS (BALANCED PANEL 1)
********************************************************************************
frame copy default balance_regs, replace
frame change balance_regs	
*	
*Creating balanced panel samples
preserve
keep station_id date retail  
tsfill //Double-checking balance
gen month=month(date)
keep if month>=6 & month<=10
gen report=1 if !missing(retail)
bys station_id: egen report_tot=total(report) 
gen tot=1 
bys station_id: egen tot_n=total(tot)  
gen report_day=report_tot/tot_n
gen week=week(date)
gen year=year(date)
gen yw=yw(year, week)
gcollapse (mean) retail report_day (first) month, by(yw station_id)
xtset station_id yw
tsfill //Double-checking balance
keep if month>=6 & month<=10
gen report=1 if !missing(retail)
bys station_id: egen report_tot=total(report) 
gen tot=1 
bys station_id: egen tot_n=total(tot)  
gen report_week=report_tot/tot_n
keep station_id report_week report_day
gduplicates drop 
save $data_clean/balanced_panel_i, replace
restore
*
gsort station_id date
merge m:1 station_id using $data_clean/balanced_panel_i
drop _merge

*****************
*PANEL (A): NO UPSTREAM PRICE CONTROLS 
eststo clear 		   
*Column 1 - Retail 1
reghdfe retail pre_hur hur post_hur CTST CHRT CTSN CHRN temp temp2 if report_day>=0.95, ///
			   absorb(state_name year month dow) cluster(county_FIPS)	   
	gdistinct station_id if e(sample)
	estadd scalar n_stats = r(ndistinct) 
	estadd local state_fe "Yes"
	estadd local station_fe "No"
	estadd local year_fe "Yes"
	estadd local month_fe "Yes"
	estadd local dow_fe "Yes"
est store A
			   
*Column 2 - Retail 2
reghdfe retail pre_hur hur post_hur CTST CHRT CTSN CHRN temp temp2 if report_day>=0.95, ///
			   absorb(station_id year month dow) cluster(county_FIPS)	
	gdistinct station_id if e(sample)
	estadd scalar n_stats = r(ndistinct) 
	estadd local state_fe "No"
	estadd local station_fe "Yes"
	estadd local year_fe "Yes"
	estadd local month_fe "Yes"	
	estadd local dow_fe "Yes"	
est store B

*Column 3 - Wholesale 1
	reghdfe wholesale pre_hur hur post_hur CTST CHRT CTSN CHRN temp temp2 if report_day>=0.95, ///
			   absorb(state_name year month dow) cluster(nearRack1)
	gdistinct nearRack1 if e(sample)
	estadd scalar n_stats = r(ndistinct) 
	estadd local state_fe "Yes"
	estadd local station_fe "No"
	estadd local year_fe "Yes"
	estadd local month_fe "Yes"
	estadd local dow_fe "Yes"	
est store C
			   
*Column 4 - Wholesale 2
reghdfe wholesale pre_hur hur post_hur CTST CHRT CTSN CHRN temp temp2 if report_day>=0.95, ///
			   absorb(nearRack1 year month dow) cluster(nearRack1)		
	gdistinct nearRack1 if e(sample)
	estadd scalar n_stats = r(ndistinct) 
	estadd local state_fe "No"
	estadd local station_fe "Yes"
	estadd local year_fe "Yes"
	estadd local month_fe "Yes"	
	estadd local dow_fe "Yes"	
est store D

esttab A B C D using "$output/price_regs_balance_day1a_$outputdate.csv", replace label ///
     b(a2) nonumber ///
    starlevels(* 0.10 ** 0.05 *** 0.01) ///
	title(Average Effect of Hurricanes on Retail Prices, Wholesale Prices, and Margins)  ///
	cells(b(fmt(3) star) se(fmt(3) par)) ///
	drop(_cons CTST CHRT CTSN CHRN temp temp2) ///
	note(Notes: The dependent variable is station-level retail/wholesale ///
	     price. "Pre-Hurricane" is an indicator variable for whether a station ///
		 lies in an area impacted by a hurricane or coastal area five ///
		 days before a hurricane warning was issued. "Hurricane" and ///
		 "Post-Hurricane" are similar indicator variables for days during a ///
		 hurricane warning and the five-days after a hurricane warning, ///
		 respectively. Standard errors are clustered at the ///
		 county for retail regressions and wholesale rack for wholesale ///
		 regressions. *, **, and ***   denote significance at ///
		 the 10\%, 5\%, and 1\% level.) ///
	coef(pre_hur "Pre-Hurricane" hur "Hurricane" post_hur "Post-Hurricane") ///
	scalars("n_stats Stations/Racks" "state_fe State FE"  ///
	        "station_fe Station/Rack FE" "year_fe Year FE" ///
			"month_fe Month-of-Year FE" "dow_fe Day-of-Week FE")  ///
			 sfmt(%8.0f) mlabels((1) (2) (3) (4)) collabels(none) 

*****************
*PANEL (B): UPSTREAM PRICE CONTROLS 
eststo clear 		   
*Column 1 - Retail 1
reghdfe retail pre_hur hur post_hur wholesale CTST CHRT CTSN CHRN temp temp2 if report_day>=0.95, ///
			   absorb(state_name year month dow) cluster(county_FIPS)	   
	gdistinct station_id if e(sample)
	estadd scalar n_stats = r(ndistinct) 
	estadd local state_fe "Yes"
	estadd local station_fe "No"
	estadd local year_fe "Yes"
	estadd local month_fe "Yes"
	estadd local dow_fe "Yes"	
est store A
			   
*Column 2 - Retail 2
reghdfe retail pre_hur hur post_hur wholesale CTST CHRT CTSN CHRN temp temp2 if report_day>=0.95, ///
			   absorb(station_id year month dow) cluster(county_FIPS)			   
	gdistinct station_id if e(sample)
	estadd scalar n_stats = r(ndistinct) 
	estadd local state_fe "No"
	estadd local station_fe "Yes"
	estadd local year_fe "Yes"
	estadd local month_fe "Yes"	
	estadd local dow_fe "Yes"	
est store B

*Column 3 - Wholesale 1
reghdfe wholesale pre_hur hur post_hur bulk CTST CHRT CTSN CHRN temp temp2 if report_day>=0.95, ///
			   absorb(state_name year month) cluster(nearRack1)
	gdistinct nearRack1 if e(sample)
	estadd scalar n_stats = r(ndistinct) 
	estadd local state_fe "Yes"
	estadd local station_fe "No"
	estadd local year_fe "Yes"
	estadd local month_fe "Yes"
	estadd local dow_fe "Yes"	
est store C
			   
*Column 4 - Wholesale 2
reghdfe wholesale pre_hur hur post_hur bulk CTST CHRT CTSN CHRN temp temp2 if report_day>=0.95, ///
			   absorb(nearRack1 year month) cluster(nearRack1)		
	gdistinct nearRack1 if e(sample)
	estadd scalar n_stats = r(ndistinct) 
	estadd local state_fe "No"
	estadd local station_fe "Yes"
	estadd local year_fe "Yes"
	estadd local month_fe "Yes"		
	estadd local dow_fe "Yes"	
est store D

esttab A B C D using "$output/price_regs_balance_day1b_$outputdate.csv", replace label ///
     b(a2) nonumber ///
    starlevels(* 0.10 ** 0.05 *** 0.01) ///
	title(Average Effect of Hurricanes on Retail Prices, Wholesale Prices, and Margins  \label{tab:price_main1b})  ///
	cells(b(fmt(3) star) se(fmt(3) par)) ///
	drop(_cons CTST CHRT CTSN CHRN temp temp2) ///
	note(Notes: The dependent variable is station-level retail/wholesale ///
	     price. "Pre-Hurricane" is an indicator variable for whether a station ///
		 lies in an area impacted by a hurricane or coastal area five ///
		 days before a hurricane warning was issued. "Hurricane" and ///
		 "Post-Hurricane" are similar indicator variables for days during a ///
		 hurricane warning and the five-days after a hurricane warning, ///
		 respectively. Standard errors are clustered at the ///
		 county for retail regressions and wholesale rack for wholesale ///
		 regressions. *, **, and ***   denote significance at ///
		 the 10\%, 5\%, and 1\% level.) ///
	coef(pre_hur "Pre-Hurricane" hur "Hurricane" post_hur "Post-Hurricane") ///
	scalars("n_stats Stations/Racks" "state_fe State FE"  ///
	        "station_fe Station/Rack FE" "year_fe Year FE" ///
			"month_fe Month-of-Year FE" "dow_fe Day-of-Week FE")  ///
			 sfmt(%8.0f) mlabels((1) (2) (3) (4)) collabels(none) 
			 
		 
********************************************************************************
*TABLE B.3: AVERAGE EFFECT OF HURRICANES ON RETAIL AND WHOLESALE PRICES AND MARGINS (BALANCED PANEL 2)
********************************************************************************
*****************
*PANEL (A): NO UPSTREAM PRICE CONTROLS 
eststo clear 		   
*Column 1 - Retail 1
reghdfe retail pre_hur hur post_hur CTST CHRT CTSN CHRN temp temp2 if report_week==1, ///
			   absorb(state_name year month dow) cluster(county_FIPS)	   
	gdistinct station_id if e(sample)
	estadd scalar n_stats = r(ndistinct) 
	estadd local state_fe "Yes"
	estadd local station_fe "No"
	estadd local year_fe "Yes"
	estadd local month_fe "Yes"
	estadd local dow_fe "Yes"
est store A
			   
*Column 2 - Retail 2
reghdfe retail pre_hur hur post_hur CTST CHRT CTSN CHRN temp temp2 if report_week==1, ///
			   absorb(station_id year month dow) cluster(county_FIPS)	
	gdistinct station_id if e(sample)
	estadd scalar n_stats = r(ndistinct) 
	estadd local state_fe "No"
	estadd local station_fe "Yes"
	estadd local year_fe "Yes"
	estadd local month_fe "Yes"	
	estadd local dow_fe "Yes"	
est store B

*Column 3 - Wholesale 1
	reghdfe wholesale pre_hur hur post_hur CTST CHRT CTSN CHRN temp temp2 if report_week==1, ///
			   absorb(state_name year month dow) cluster(nearRack1)
	gdistinct nearRack1 if e(sample)
	estadd scalar n_stats = r(ndistinct) 
	estadd local state_fe "Yes"
	estadd local station_fe "No"
	estadd local year_fe "Yes"
	estadd local month_fe "Yes"
	estadd local dow_fe "Yes"	
est store C
			   
*Column 4 - Wholesale 2
reghdfe wholesale pre_hur hur post_hur CTST CHRT CTSN CHRN temp temp2 if report_week==1, ///
			   absorb(nearRack1 year month dow) cluster(nearRack1)		
	gdistinct nearRack1 if e(sample)
	estadd scalar n_stats = r(ndistinct) 
	estadd local state_fe "No"
	estadd local station_fe "Yes"
	estadd local year_fe "Yes"
	estadd local month_fe "Yes"	
	estadd local dow_fe "Yes"	
est store D

esttab A B C D using "$output/price_regs_balance_week1a_$outputdate.csv", replace label ///
     b(a2) nonumber ///
    starlevels(* 0.10 ** 0.05 *** 0.01) ///
	title(Average Effect of Hurricanes on Retail Prices, Wholesale Prices, and Margins)  ///
	cells(b(fmt(3) star) se(fmt(3) par)) ///
	drop(_cons CTST CHRT CTSN CHRN temp temp2) ///
	note(Notes: The dependent variable is station-level retail/wholesale ///
	     price. "Pre-Hurricane" is an indicator variable for whether a station ///
		 lies in an area impacted by a hurricane or coastal area five ///
		 days before a hurricane warning was issued. "Hurricane" and ///
		 "Post-Hurricane" are similar indicator variables for days during a ///
		 hurricane warning and the five-days after a hurricane warning, ///
		 respectively. Standard errors are clustered at the ///
		 county for retail regressions and wholesale rack for wholesale ///
		 regressions. *, **, and ***   denote significance at ///
		 the 10\%, 5\%, and 1\% level.) ///
	coef(pre_hur "Pre-Hurricane" hur "Hurricane" post_hur "Post-Hurricane") ///
	scalars("n_stats Stations/Racks" "state_fe State FE"  ///
	        "station_fe Station/Rack FE" "year_fe Year FE" ///
			"month_fe Month-of-Year FE" "dow_fe Day-of-Week FE")  ///
			 sfmt(%8.0f) mlabels((1) (2) (3) (4)) collabels(none) 

*****************
*PANEL (A): UPSTREAM PRICE CONTROLS 
eststo clear 		   
*Column 1 - Retail 1
reghdfe retail pre_hur hur post_hur wholesale CTST CHRT CTSN CHRN temp temp2 if report_week==1, ///
			   absorb(state_name year month dow) cluster(county_FIPS)	   
	gdistinct station_id if e(sample)
	estadd scalar n_stats = r(ndistinct) 
	estadd local state_fe "Yes"
	estadd local station_fe "No"
	estadd local year_fe "Yes"
	estadd local month_fe "Yes"
	estadd local dow_fe "Yes"	
est store A
			   
*Column 2 - Retail 2
reghdfe retail pre_hur hur post_hur wholesale CTST CHRT CTSN CHRN temp temp2 if report_week==1, ///
			   absorb(station_id year month dow) cluster(county_FIPS)			   
	gdistinct station_id if e(sample)
	estadd scalar n_stats = r(ndistinct) 
	estadd local state_fe "No"
	estadd local station_fe "Yes"
	estadd local year_fe "Yes"
	estadd local month_fe "Yes"	
	estadd local dow_fe "Yes"	
est store B

*Column 3 - Wholesale 1
reghdfe wholesale pre_hur hur post_hur bulk CTST CHRT CTSN CHRN temp temp2 if report_week==1, ///
			   absorb(state_name year month) cluster(nearRack1)
	gdistinct nearRack1 if e(sample)
	estadd scalar n_stats = r(ndistinct) 
	estadd local state_fe "Yes"
	estadd local station_fe "No"
	estadd local year_fe "Yes"
	estadd local month_fe "Yes"
	estadd local dow_fe "Yes"	
est store C
			   
*Column 4 - Wholesale 2
reghdfe wholesale pre_hur hur post_hur bulk CTST CHRT CTSN CHRN temp temp2 if report_week==1, ///
			   absorb(nearRack1 year month) cluster(nearRack1)		
	gdistinct nearRack1 if e(sample)
	estadd scalar n_stats = r(ndistinct) 
	estadd local state_fe "No"
	estadd local station_fe "Yes"
	estadd local year_fe "Yes"
	estadd local month_fe "Yes"		
	estadd local dow_fe "Yes"	
est store D

esttab A B C D using "$output/price_regs_balance_week1b_$outputdate.csv", replace label ///
     b(a2) nonumber ///
    starlevels(* 0.10 ** 0.05 *** 0.01) ///
	title(Average Effect of Hurricanes on Retail Prices, Wholesale Prices, and Margins  \label{tab:price_main1b})  ///
	cells(b(fmt(3) star) se(fmt(3) par)) ///
	drop(_cons CTST CHRT CTSN CHRN temp temp2) ///
	note(Notes: The dependent variable is station-level retail/wholesale ///
	     price. "Pre-Hurricane" is an indicator variable for whether a station ///
		 lies in an area impacted by a hurricane or coastal area five ///
		 days before a hurricane warning was issued. "Hurricane" and ///
		 "Post-Hurricane" are similar indicator variables for days during a ///
		 hurricane warning and the five-days after a hurricane warning, ///
		 respectively. Standard errors are clustered at the ///
		 county for retail regressions and wholesale rack for wholesale ///
		 regressions. *, **, and ***   denote significance at ///
		 the 10\%, 5\%, and 1\% level.) ///
	coef(pre_hur "Pre-Hurricane" hur "Hurricane" post_hur "Post-Hurricane") ///
	scalars("n_stats Stations/Racks" "state_fe State FE"  ///
	        "station_fe Station/Rack FE" "year_fe Year FE" ///
			"month_fe Month-of-Year FE" "dow_fe Day-of-Week FE")  ///
			 sfmt(%8.0f) mlabels((1) (2) (3) (4)) collabels(none) 
frame change default			 
		 

********************************************************************************
*TABLE B.4: AVERAGE EFFECT OF HURRICANES ON RETAIL AND WHOLESALE PRICES AND MARGINS (ADJUSTED STANDARD ERRORS)
********************************************************************************
frame copy default regs_std_error, replace
frame change regs_std_error
keep if sample_main==1
gsort station_id date
replace county_FIPS = 1 if coast_pfz==1 & state_code=="FL"
replace county_FIPS = 2 if coast_pfz==1 & state_code=="LA"
*
*****************
*PANEL (A): NO UPSTREAM PRICE CONTROLS (COASTAL STD ERRORS)
eststo clear 		   
*Column 1 - Retail 1
reghdfe retail pre_hur hur post_hur CTST CHRT CTSN CHRN temp temp2, ///
			   absorb(state_name year month dow) cluster(county_FIPS)	   
	gdistinct station_id if e(sample)
	estadd scalar n_stats = r(ndistinct) 
	estadd local state_fe "Yes"
	estadd local station_fe "No"
	estadd local year_fe "Yes"
	estadd local month_fe "Yes"
	estadd local dow_fe "Yes"
est store A
			   
*Column 2 - Retail 2
reghdfe retail pre_hur hur post_hur CTST CHRT CTSN CHRN temp temp2, ///
			   absorb(station_id year month dow) cluster(county_FIPS)	
	gdistinct station_id if e(sample)
	estadd scalar n_stats = r(ndistinct) 
	estadd local state_fe "No"
	estadd local station_fe "Yes"
	estadd local year_fe "Yes"
	estadd local month_fe "Yes"	
	estadd local dow_fe "Yes"	
est store B

*Column 3 - Wholesale 1
	reghdfe wholesale pre_hur hur post_hur CTST CHRT CTSN CHRN temp temp2, ///
			   absorb(state_name year month dow) cluster(nearRack1)
	gdistinct nearRack1 if e(sample)
	estadd scalar n_stats = r(ndistinct) 
	estadd local state_fe "Yes"
	estadd local station_fe "No"
	estadd local year_fe "Yes"
	estadd local month_fe "Yes"
	estadd local dow_fe "Yes"	
est store C
			   
*Column 4 - Wholesale 2
reghdfe wholesale pre_hur hur post_hur CTST CHRT CTSN CHRN temp temp2, ///
			   absorb(nearRack1 year month dow) cluster(nearRack1)		
	gdistinct nearRack1 if e(sample)
	estadd scalar n_stats = r(ndistinct) 
	estadd local state_fe "No"
	estadd local station_fe "Yes"
	estadd local year_fe "Yes"
	estadd local month_fe "Yes"	
	estadd local dow_fe "Yes"	
est store D

esttab A B C D using "$output/price_coastal_ses_main1a_$outputdate.csv", replace label ///
     b(a2) nonumber ///
    starlevels(* 0.10 ** 0.05 *** 0.01) ///
	title(Average Effect of Hurricanes on Retail Prices, Wholesale Prices, and Margins)  ///
	cells(b(fmt(3) star) se(fmt(3) par)) ///
	drop(_cons CTST CHRT CTSN CHRN temp temp2) ///
	note(Notes: The dependent variable is station-level retail/wholesale ///
	     price. "Pre-Hurricane" is an indicator variable for whether a station ///
		 lies in an area impacted by a hurricane or coastal area five ///
		 days before a hurricane warning was issued. "Hurricane" and ///
		 "Post-Hurricane" are similar indicator variables for days during a ///
		 hurricane warning and the five-days after a hurricane warning, ///
		 respectively. Standard errors are clustered at the ///
		 county for retail regressions and wholesale rack for wholesale ///
		 regressions. *, **, and ***   denote significance at ///
		 the 10\%, 5\%, and 1\% level.) ///
	coef(pre_hur "Pre-Hurricane" hur "Hurricane" post_hur "Post-Hurricane") ///
	scalars("n_stats Stations/Racks" "state_fe State FE"  ///
	        "station_fe Station/Rack FE" "year_fe Year FE" ///
			"month_fe Month-of-Year FE" "dow_fe Day-of-Week FE")  ///
			 sfmt(%8.0f) mlabels((1) (2) (3) (4)) collabels(none) 
*
*****************
*PANEL (B): UPSTREAM PRICE CONTROLS (COASTAL STD ERRORS)
eststo clear 		   
*Column 1 - Retail 1
reghdfe retail pre_hur hur post_hur wholesale CTST CHRT CTSN CHRN temp temp2, ///
			   absorb(state_name year month dow) cluster(county_FIPS)	   
	gdistinct station_id if e(sample)
	estadd scalar n_stats = r(ndistinct) 
	estadd local state_fe "Yes"
	estadd local station_fe "No"
	estadd local year_fe "Yes"
	estadd local month_fe "Yes"
	estadd local dow_fe "Yes"	
est store A
			   
*Column 2 - Retail 2
reghdfe retail pre_hur hur post_hur wholesale CTST CHRT CTSN CHRN temp temp2, ///
			   absorb(station_id year month dow) cluster(county_FIPS)			   
	gdistinct station_id if e(sample)
	estadd scalar n_stats = r(ndistinct) 
	estadd local state_fe "No"
	estadd local station_fe "Yes"
	estadd local year_fe "Yes"
	estadd local month_fe "Yes"	
	estadd local dow_fe "Yes"	
est store B

*Column 3 - Wholesale 1
reghdfe wholesale pre_hur hur post_hur bulk CTST CHRT CTSN CHRN temp temp2, ///
			   absorb(state_name year month) cluster(nearRack1)
	gdistinct nearRack1 if e(sample)
	estadd scalar n_stats = r(ndistinct) 
	estadd local state_fe "Yes"
	estadd local station_fe "No"
	estadd local year_fe "Yes"
	estadd local month_fe "Yes"
	estadd local dow_fe "Yes"	
est store C
			   
*Column 4 - Wholesale 2
reghdfe wholesale pre_hur hur post_hur bulk CTST CHRT CTSN CHRN temp temp2, ///
			   absorb(nearRack1 year month) cluster(nearRack1) 		
	gdistinct nearRack1 if e(sample)
	estadd scalar n_stats = r(ndistinct) 
	estadd local state_fe "No"
	estadd local station_fe "Yes"
	estadd local year_fe "Yes"
	estadd local month_fe "Yes"		
	estadd local dow_fe "Yes"	
est store D

esttab A B C D using "$output/price_coastal_ses_main1b_$outputdate.csv", replace label ///
     b(a2) nonumber ///
    starlevels(* 0.10 ** 0.05 *** 0.01) ///
	title(Average Effect of Hurricanes on Retail Prices, Wholesale Prices, and Margins  \label{tab:price_main1b})  ///
	cells(b(fmt(3) star) se(fmt(3) par)) ///
	drop(_cons CTST CHRT CTSN CHRN temp temp2) ///
	note(Notes: The dependent variable is station-level retail/wholesale ///
	     price. "Pre-Hurricane" is an indicator variable for whether a station ///
		 lies in an area impacted by a hurricane or coastal area five ///
		 days before a hurricane warning was issued. "Hurricane" and ///
		 "Post-Hurricane" are similar indicator variables for days during a ///
		 hurricane warning and the five-days after a hurricane warning, ///
		 respectively. Standard errors are clustered at the ///
		 county for retail regressions and wholesale rack for wholesale ///
		 regressions. *, **, and ***   denote significance at ///
		 the 10\%, 5\%, and 1\% level.) ///
	coef(pre_hur "Pre-Hurricane" hur "Hurricane" post_hur "Post-Hurricane") ///
	scalars("n_stats Stations/Racks" "state_fe State FE"  ///
	        "station_fe Station/Rack FE" "year_fe Year FE" ///
			"month_fe Month-of-Year FE" "dow_fe Day-of-Week FE")  ///
			 sfmt(%8.0f) mlabels((1) (2) (3) (4)) collabels(none) 
*
*****************
*PANEL (A): NO UPSTREAM PRICE CONTROLS (TWO-WAY STD ERRORS)
eststo clear 		   
*Column 1 - Retail 1
reghdfe retail pre_hur hur post_hur CTST CHRT CTSN CHRN temp temp2, ///
			   absorb(state_name year month dow) cluster(county_FIPS ym)	   
	gdistinct station_id if e(sample)
	estadd scalar n_stats = r(ndistinct) 
	estadd local state_fe "Yes"
	estadd local station_fe "No"
	estadd local year_fe "Yes"
	estadd local month_fe "Yes"
	estadd local dow_fe "Yes"
est store A
			   
*Column 2 - Retail 2
reghdfe retail pre_hur hur post_hur CTST CHRT CTSN CHRN temp temp2, ///
			   absorb(station_id year month dow) cluster(county_FIPS ym)	
	gdistinct station_id if e(sample)
	estadd scalar n_stats = r(ndistinct) 
	estadd local state_fe "No"
	estadd local station_fe "Yes"
	estadd local year_fe "Yes"
	estadd local month_fe "Yes"	
	estadd local dow_fe "Yes"	
est store B

*Column 3 - Wholesale 1
	reghdfe wholesale pre_hur hur post_hur CTST CHRT CTSN CHRN temp temp2, ///
			   absorb(state_name year month dow) cluster(nearRack1 ym)
	gdistinct nearRack1 if e(sample)
	estadd scalar n_stats = r(ndistinct) 
	estadd local state_fe "Yes"
	estadd local station_fe "No"
	estadd local year_fe "Yes"
	estadd local month_fe "Yes"
	estadd local dow_fe "Yes"	
est store C
			   
*Column 4 - Wholesale 2
reghdfe wholesale pre_hur hur post_hur CTST CHRT CTSN CHRN temp temp2, ///
			   absorb(nearRack1 year month dow) cluster(nearRack1 ym)		
	gdistinct nearRack1 if e(sample)
	estadd scalar n_stats = r(ndistinct) 
	estadd local state_fe "No"
	estadd local station_fe "Yes"
	estadd local year_fe "Yes"
	estadd local month_fe "Yes"	
	estadd local dow_fe "Yes"	
est store D

esttab A B C D using "$output/price_tw_ses_main1a_$outputdate.csv", replace label ///
     b(a2) nonumber ///
    starlevels(* 0.10 ** 0.05 *** 0.01) ///
	title(Average Effect of Hurricanes on Retail Prices, Wholesale Prices, and Margins)  ///
	cells(b(fmt(3) star) se(fmt(3) par)) ///
	drop(_cons CTST CHRT CTSN CHRN temp temp2) ///
	note(Notes: The dependent variable is station-level retail/wholesale ///
	     price. "Pre-Hurricane" is an indicator variable for whether a station ///
		 lies in an area impacted by a hurricane or coastal area five ///
		 days before a hurricane warning was issued. "Hurricane" and ///
		 "Post-Hurricane" are similar indicator variables for days during a ///
		 hurricane warning and the five-days after a hurricane warning, ///
		 respectively. Standard errors are clustered at the ///
		 county for retail regressions and wholesale rack for wholesale ///
		 regressions. *, **, and ***   denote significance at ///
		 the 10\%, 5\%, and 1\% level.) ///
	coef(pre_hur "Pre-Hurricane" hur "Hurricane" post_hur "Post-Hurricane") ///
	scalars("n_stats Stations/Racks" "state_fe State FE"  ///
	        "station_fe Station/Rack FE" "year_fe Year FE" ///
			"month_fe Month-of-Year FE" "dow_fe Day-of-Week FE")  ///
			 sfmt(%8.0f) mlabels((1) (2) (3) (4)) collabels(none) 
*
*****************
*PANEL (B): UPSTREAM PRICE CONTROLS (TWO-WAY STD ERRORS)
eststo clear 		   
*Column 1 - Retail 1
reghdfe retail pre_hur hur post_hur wholesale CTST CHRT CTSN CHRN temp temp2, ///
			   absorb(state_name year month dow) cluster(county_FIPS ym)	   
	gdistinct station_id if e(sample)
	estadd scalar n_stats = r(ndistinct) 
	estadd local state_fe "Yes"
	estadd local station_fe "No"
	estadd local year_fe "Yes"
	estadd local month_fe "Yes"
	estadd local dow_fe "Yes"	
est store A
			   
*Column 2 - Retail 2
reghdfe retail pre_hur hur post_hur wholesale CTST CHRT CTSN CHRN temp temp2, ///
			   absorb(station_id year month dow) cluster(county_FIPS ym)			   
	gdistinct station_id if e(sample)
	estadd scalar n_stats = r(ndistinct) 
	estadd local state_fe "No"
	estadd local station_fe "Yes"
	estadd local year_fe "Yes"
	estadd local month_fe "Yes"	
	estadd local dow_fe "Yes"	
est store B

*Column 3 - Wholesale 1
reghdfe wholesale pre_hur hur post_hur bulk CTST CHRT CTSN CHRN temp temp2, ///
			   absorb(state_name year month) cluster(nearRack1 ym)
	gdistinct nearRack1 if e(sample)
	estadd scalar n_stats = r(ndistinct) 
	estadd local state_fe "Yes"
	estadd local station_fe "No"
	estadd local year_fe "Yes"
	estadd local month_fe "Yes"
	estadd local dow_fe "Yes"	
est store C
			   
*Column 4 - Wholesale 2
reghdfe wholesale pre_hur hur post_hur bulk CTST CHRT CTSN CHRN temp temp2, ///
			   absorb(nearRack1 year month) cluster(nearRack1 ym) 		
	gdistinct nearRack1 if e(sample)
	estadd scalar n_stats = r(ndistinct) 
	estadd local state_fe "No"
	estadd local station_fe "Yes"
	estadd local year_fe "Yes"
	estadd local month_fe "Yes"		
	estadd local dow_fe "Yes"	
est store D

esttab A B C D using "$output/price_tw_ses_main1b_$outputdate.csv", replace label ///
     b(a2) nonumber ///
    starlevels(* 0.10 ** 0.05 *** 0.01) ///
	title(Average Effect of Hurricanes on Retail Prices, Wholesale Prices, and Margins  \label{tab:price_main1b})  ///
	cells(b(fmt(3) star) se(fmt(3) par)) ///
	drop(_cons CTST CHRT CTSN CHRN temp temp2) ///
	note(Notes: The dependent variable is station-level retail/wholesale ///
	     price. "Pre-Hurricane" is an indicator variable for whether a station ///
		 lies in an area impacted by a hurricane or coastal area five ///
		 days before a hurricane warning was issued. "Hurricane" and ///
		 "Post-Hurricane" are similar indicator variables for days during a ///
		 hurricane warning and the five-days after a hurricane warning, ///
		 respectively. Standard errors are clustered at the ///
		 county for retail regressions and wholesale rack for wholesale ///
		 regressions. *, **, and ***   denote significance at ///
		 the 10\%, 5\%, and 1\% level.) ///
	coef(pre_hur "Pre-Hurricane" hur "Hurricane" post_hur "Post-Hurricane") ///
	scalars("n_stats Stations/Racks" "state_fe State FE"  ///
	        "station_fe Station/Rack FE" "year_fe Year FE" ///
			"month_fe Month-of-Year FE" "dow_fe Day-of-Week FE")  ///
			 sfmt(%8.0f) mlabels((1) (2) (3) (4)) collabels(none) 		 
frame change default
	
	
********************************************************************************
*TABLE B.5: AVERAGE EFFECT OF HURRICANES ON RETAIL AND WHOLESALE PRICES AND MARGINS - LAGGED WHOLESALE AND BULK CONTROLS
********************************************************************************	
frame copy default regs_lag, replace
frame change regs_lag
keep if sample_main==1
gsort station_id date
*
eststo clear 		   
*Column 1 - Retail 1
reghdfe retail pre_hur hur post_hur  l(0/29).d.wholesale l30.wholesale CTST CHRT CTSN CHRN temp temp2, ///
			   absorb(state_name year month dow) cluster(county_FIPS)	   
	gdistinct station_id if e(sample)
	estadd scalar n_stats = r(ndistinct) 
	estadd local state_fe "Yes"
	estadd local station_fe "No"
	estadd local year_fe "Yes"
	estadd local month_fe "Yes"
	estadd local dow_fe "Yes"	
est store A
			   
*Column 2 - Retail 2
reghdfe retail pre_hur hur post_hur l(0/29).d.wholesale l30.wholesale CTST CHRT CTSN CHRN temp temp2, ///
			   absorb(station_id year month dow) cluster(county_FIPS)			   
	gdistinct station_id if e(sample)
	estadd scalar n_stats = r(ndistinct) 
	estadd local state_fe "No"
	estadd local station_fe "Yes"
	estadd local year_fe "Yes"
	estadd local month_fe "Yes"	
	estadd local dow_fe "Yes"	
est store B

*Column 3 - Wholesale 1
reghdfe wholesale pre_hur hur post_hur  l(0/29).d.bulk l30.bulk CTST CHRT CTSN CHRN temp temp2, ///
			   absorb(state_name year month) cluster(nearRack1)
	gdistinct nearRack1 if e(sample)
	estadd scalar n_stats = r(ndistinct) 
	estadd local state_fe "Yes"
	estadd local station_fe "No"
	estadd local year_fe "Yes"
	estadd local month_fe "Yes"
	estadd local dow_fe "Yes"	
est store C
			   
*Column 4 - Wholesale 2
reghdfe wholesale pre_hur hur post_hur  l(0/29).d.bulk l30.bulk CTST CHRT CTSN CHRN temp temp2, ///
			   absorb(nearRack1 year month) cluster(nearRack1)		
	gdistinct nearRack1 if e(sample)
	estadd scalar n_stats = r(ndistinct) 
	estadd local state_fe "No"
	estadd local station_fe "Yes"
	estadd local year_fe "Yes"
	estadd local month_fe "Yes"		
	estadd local dow_fe "Yes"	
est store D

esttab A B C D using "$output/price_regs_distlag_$outputdate.csv", replace label ///
     b(a2) nonumber ///
    starlevels(* 0.10 ** 0.05 *** 0.01) ///
	title(Average Effect of Hurricanes on Retail Prices, Wholesale Prices, and Margins  \label{tab:price_main1b})  ///
	cells(b(fmt(3) star) se(fmt(3) par)) ///
	drop(_cons CTST CHRT CTSN CHRN temp temp2 LD*) ///
	note(Notes: The dependent variable is station-level retail/wholesale ///
	     price. "Pre-Hurricane" is an indicator variable for whether a station ///
		 lies in an area impacted by a hurricane or coastal area five ///
		 days before a hurricane warning was issued. "Hurricane" and ///
		 "Post-Hurricane" are similar indicator variables for days during a ///
		 hurricane warning and the five-days after a hurricane warning, ///
		 respectively. Standard errors are clustered at the ///
		 county for retail regressions and wholesale rack for wholesale ///
		 regressions. *, **, and ***   denote significance at ///
		 the 10\%, 5\%, and 1\% level.) ///
	coef(pre_hur "Pre-Hurricane" hur "Hurricane" post_hur "Post-Hurricane") ///
	scalars("n_stats Stations/Racks" "state_fe State FE"  ///
	        "station_fe Station/Rack FE" "year_fe Year FE" ///
			"month_fe Month-of-Year FE" "dow_fe Day-of-Week FE")  ///
			 sfmt(%8.0f) mlabels((1) (2) (3) (4)) collabels(none) 
frame change default